The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index; none of the 75 sources its claims cite is checked for support yet.
"How advertiser-friendly is my video?": YouTuber's Socioeconomic Interactions with Algorithmic Content Moderation
Open accessAbstract
To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income. Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation. Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity. YouTubers coped with such challenges from algorithmic moderation by sharing and applying practical knowledge they learned about moderation algorithms. By analyzing video content creation as algorithmic labor, we unpack the socioeconomic implications of algorithmic moderation and point to necessary post-punishment support as a form of restorative justice. Lastly, we put forward design considerations for algorithmic moderation systems.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper analyzes r/youtube discussions to show how YouTube’s algorithmic content moderation, especially demonetization, creates opacity and precarity for YouTubers, who respond by collectively learning and applying algorithmic know-how. It argues for restorative justice via peer/platform support and design changes such as more specific explanations, historical-content-aware classifiers, and compensation for false demonetization.
Key findings
- Across the final dataset of 3,086 threads and 36,279 comments (June 8, 2012–December 23, 2020; average ~21.82 comments per thread), YouTubers described algorithmic opacity at multiple layers: no warnings, generic explanations, irregular re-flagging, appeals answered in as little as “3 minutes” for a 20,000-subscriber channel, and perceived uneven enforcement between small and large channels.
- Demonetization produced algorithmic precarity: one YouTuber with 155,000 subscribers said over half their videos were marked and ad revenue dropped close to 90%, to about 1/4 prior revenue; another reported losing over $10,000 in revenue and feeling depressed and anxious; temporary demonetization caused lost ad income during manual review waits.
- YouTubers coped reflexively by sharing and applying algorithmic know-how: metadata (tags, title, description, thumbnail) may trigger moderation; they tweaked metadata to restore monetization, strategically appealed, offset upload schedules by 2–3 days, diversified income via Patreon/Ko-fi, contacted MCNs, or migrated to other platforms like Bitchute.
- The paper frames peer and platform support as restorative justice and proposes design considerations: specific explanations (e.g., thumbnail-caused demonetization), classifiers using historical content, and compensation for falsely demonetized videos.
Methods
- Qualitative study of the r/youtube subreddit. Using RedditExtractoR on R 4.0.4, the authors iteratively fetched threads by keywords drawn from moderation literature and media reports; the initial dataset contained 2,779 threads and 60,310 comments, and after adding contextual keywords, removing comments under 50 characters, and removing irrelevant threads, the final dataset contained 3,086 threads and 36,279 comments.
- Inductive thematic analysis with open, axial, and selective coding; the authors focused on posts explicitly identifying posters as YouTubers or audiences and distilled three themes: “Being Confused in Algorithmic Opacity,” “Managing Algorithmic Precarity,” and “Learning and Applying Algorithmic Know-How.”
- Ethics: IRB approved; usernames and post URLs were removed, quotes were paraphrased, and singular “they” was used to reduce searchability.
Limitations
- Paper-stated: subreddit data may contain biased or conflicting information; people who find moderation punishments fair may not post; keyword-based collection misses implicit expressions; the dataset lacks YouTuber category information, so differences by creator type cannot be examined; human moderation is not discussed because YouTube relies on automatic moderation for more than 95% of cases; future surveys or interviews are suggested.
- Paper-stated: the study is exploratory and presents balanced interpretations from Reddit data rather than platform-side or algorithmic access.
- Evident from the text: self-reported figures such as subscriber counts, revenue losses, and moderation experiences are unverifiable, and Reddit users are self-selected rather than representative of all YouTubers.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 75 cited sources checked · 0 supported · 0 flagged
- Findingown data
They observed a general phenomenon that YouTubers typically received similarly phrased moderation reasons.
“They also observed a general phenomenon that YouTubers typically received similarly phrased moderation reasons.”
Quote in the paperSubstantial claim - Findingreasoning
Such opacity led YouTubers' video creation work to be precarious.
“We found that opacity of algorithmic punishments existed in multiple layers, and such opacity led YouTuber's video creation work to be precarious.”
- Also stated in: § 5 FINDINGS: How do YouTubers Interact with Algorithmic Moderation? · plain
“Such algorithmic opacity led their video creation labor on YouTube to be precarious.”
Quote in the paperSubstantial claim - Also stated in: § 5 FINDINGS: How do YouTubers Interact with Algorithmic Moderation? · plain
- Findingown data
YouTubers also experienced mental stress because of demonetization in algorithmic moderation.
“YouTubers also experienced mental stress because of demonetization in algorithmic moderation.”
Quote in the paperSubstantial claim - Findingnone given
When algorithmic punishments were accompanied with explanations, they often appeared insufficient to YouTubers.
“When algorithmic punishments were accompanied with explanations, they often appeared insufficient to YouTubers.”
Quote in the paperNot a substantial claim - Findingown data
YouTubers sought to cope with moderation punishments, in a reflexive manner, by gradually gaining and applying practical knowledge of algorithms.
“Also, YouTubers sought to cope with moderation punishments, in a reflexive manner, by gradually gaining and applying practical knowledge of algorithms.”
- Also stated in: § 5 FINDINGS: How do YouTubers Interact with Algorithmic Moderation? · plain
“YouTubers then sought to handle past punishments and avoid future moderation risks, in a reflexive manner, by gaining and applying algorithmic know-how.”
Quote in the paperSubstantial claim - Also stated in: § 5 FINDINGS: How do YouTubers Interact with Algorithmic Moderation? · plain
- Findingown data
YouTubers collectively made sense of algorithmic punishments, developing and disseminating algorithmic know-how or practical knowledge of YouTube moderation.
“YouTubers collectively made sense of algorithmic punishments, developing and disseminating algorithmic know-how or practical knowledge of YouTube moderation.”
Quote in the paperSubstantial claim - Findingreasoning
Frequent complaints manifested the low confidence of explanation caused the YouTuber's negative sentiments toward the opacity of YouTube moderation.
“These frequent complaints manifested the low confidence of explanation caused the YouTuber's negative sentiments toward the opacity of YouTube moderation.”
Quote in the paperSubstantial claim - Findingown data
Many YouTubers reported that they experienced algorithmic penalties without promised warnings beforehand.
“Many YouTubers reported that they experienced algorithmic penalties without promised warnings beforehand.”
Quote in the paperSubstantial claim - Findingown data
The bot imposed penalties and corrected mistakes irregularly.
“Instead, the bot here imposed penalties and corrected mistakes irregularly.”
Quote in the paperSubstantial claim - Findingown data
YouTubers who were unsatisfied with automatic explanations had to do the work of appeal.
“In our study, YouTubers who were unsatisfied with automatic explanations had to do the work of appeal.”
Quote in the paperSubstantial claim - Findingnone given
Even holding such algorithmic knowledge, some YouTubers felt powerless to avoid future moderation risks when massive audience comments trigger demonetization punishments.
“Even holding such algorithmic knowledge, some YouTubers felt powerless to avoid future moderation risks when massive audience comments trigger demonetization punishments.”
Quote in the paperNot a substantial claim - Findingown data
Many YouTubers discussed how they could self-moderate their metadata to repair punishments.
“Many YouTubers discussed how they could self-moderate their metadata to repair punishments.”
Quote in the paperSubstantial claim - Findingown data
Moderation irregularity intensified the opacity YouTubers experienced.
“But what intensified such opacity was the moderation irregularity YouTubers experienced.”
Quote in the paperSubstantial claim - Findingown data
By editing metadata (e.g., tags, descriptions, thumbnails, user comments, and titles), a YouTuber changed their past videos to repair demonetization penalties.
“The YouTuber mentioned that by editing metadata (e.g., tags, descriptions, thumbnails, user comments, and titles), they changed their past videos to repair demonetization penalties.”
Quote in the paperSubstantial claim - Findingown data
Moderation decisions could sometimes puzzle the ordinary audience.
“Moderation decisions could sometimes puzzle the ordinary audience.”
Quote in the paperNot a substantial claim - Findingown data
Opacity of algorithmic punishments existed in multiple layers.
“We found that opacity of algorithmic punishments existed in multiple layers, and such opacity led YouTuber's video creation work to be precarious.”
- Also stated in: § 5.1 Being Confused in Algorithmic Opacity · plain
“We found that YouTubers experienced algorithmic opacity at multiple layers.”
Quote in the paperSubstantial claim - Also stated in: § 5.1 Being Confused in Algorithmic Opacity · plain
- Findingcitation
YouTubers try to mitigate moderation risks brought by problematic audience behaviors.
“First, resonating with prior studies regarding bad actors gaming algorithmic systems [23,47] and the third YouTube Adpocalypse's origins, YouTubers try to mitigate moderation risks brought by problematic audience behaviors.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [23]
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- cites [47]
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- Findingown data
This YouTuber described their experience of account suspension for violating community guidelines without being informed with detailed reasons.
“This YouTuber described their experience of account suspension for violating community guidelines without being informed with detailed reasons.”
Quote in the paperSubstantial claim - Findingown data
The waiting time of manual reviewing led to missing their deserved period for monetization.
“While their videos were turned back to monetize through the human review process, the waiting time of manual reviewing led to missing their deserved period for monetization.”
Quote in the paperSubstantial claim - Findingown data
Algorithmic moderation has negative impacts on profitability for YouTubers.
“While it was challenging to verify the exact numbers provided by the YouTuber, this quote did reveal the negative impacts of algorithmic moderation on profitability.”
Quote in the paperSubstantial claim - Findingown data
When YouTubers appealed past penalties, they often received a response inconsistent with their expected repairing process for moderation punishments.
“When YouTubers appealed past penalties, they often received a response inconsistent with their expected repairing process for moderation punishments.”
Quote in the paperSubstantial claim - Findingown data
Even though YouTube had provided keywords blocking functions for YouTubers to filter problematic audience comments, it still provided insufficient support to moderate potential commentators.
“Second, even though YouTube had provided keywords blocking functions for YouTubers to filter problematic audience comments, the example here showed that it still provided insufficient support to moderate potential commentators.”
Quote in the paperSubstantial claim - Findingown data
YouTubers diversified their income not solely to rely on ad income.
“They diversified their income not solely to rely on ad income.”
Quote in the paperSubstantial claim - Findingown data
Opaque moderation occurred when YouTubers perceived differential treatments based on their fanbase.
“Additionally, opaque moderation occurred when YouTubers perceived differential treatments based on their fanbase.”
Quote in the paperSubstantial claim - Findingcitation
The opacity also manifested in the lack of direct communicative methods to apply for human interventions on issued algorithmic punishments, unlike other social media that allowed users to directly contact moderators.
“Lastly, the opacity also manifested in the lack of direct communicative methods to apply for human interventions on issued algorithmic punishments, unlike other social media that allowed users to directly contact moderators [39,52].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [39]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [52]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingcitation
YouTubers collaborated with various funding platforms or marketing sponsors to acquire direct funding, alleviating future demonetization impacts.
“Specifically, we found that they collaborated with various funding platforms or marketing sponsors [105] to acquire direct funding, alleviating future demonetization impacts.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [105]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingcitation
Algorithmic punishments were imposed unequally between small and large video creators.
“In this quote, the YouTuber compared the thread poster's videos with several famous YouTubers' ones and described the observations where algorithmic punishments were imposed unequally between small and large video creators, also resonating with Caplan's findings [13].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingown data
Punished YouTubers often seek support from the community.
“Punished YouTubers often seek support from the community.”
Quote in the paperNot a substantial claim - Findingcitation
Because of moderation systems' complexity and insufficient explanation provision, YouTubers perceived algorithms as opaque to understand.
“Still, because of moderation systems' complexity and insufficient explanation provision, YouTubers would perceive algorithms as opaque to understand, which resonates with the opacity of algorithmic moderation that researchers found on Reddit [47,52].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [47]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [52]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingnone given
There was also a certain degree of inexplicability in executing algorithmic decisions between small and large YouTubers.
“There was also a certain degree of inexplicability in executing algorithmic decisions between small and large YouTubers.”
Quote in the paperNot a substantial claim - Findingnone given
YouTubers were confused to varied degrees due to the opacity of punishments.
“YouTubers were confused to varied degrees due to the opacity of punishments.”
Quote in the paperSubstantial claim - Findingown data
A YouTuber provided an alternative way of the appeal process and remedying past punishments: using an external social media platform to contact the YouTube staff.
“This YouTuber provided an alternative way of the appeal process and remedying past punishments: using an external social media platform to contact the YouTube staff.”
Quote in the paperNot a substantial claim - Findingown data
Some YouTubers described how they could reverse the demonetization penalty by contacting Multi-Channel Networks (MCNs).
“Some YouTubers described how they could reverse the demonetization penalty by contacting Multi-Channel Networks (MCNs), as one YouTuber wrote: MCNs can sometimes have contact with YouTube to assist with enabling monetization on channels that might normally be refused.”
Quote in the paperNot a substantial claim - Findingown data
There was a lack of warning prior to YouTube issuing algorithmic penalties.
“We showed that there was a lack of warning prior to YouTube issuing algorithmic penalties.”
Quote in the paperSubstantial claim - Findingown data
YouTubers applied the know-how about metadata moderation in their content creation.
“YouTubers applied the know-how about metadata moderation in their content creation.”
Quote in the paperSubstantial claim - Findingreasoning
The punishments could lead YouTubers to experience various degrees of severity based on personal factors such as financial dependency on video creation or mental conditions.
“The punishments could lead YouTubers to experience various degrees of severity based on personal factors such as financial dependency on video creation or mental conditions.”
Quote in the paperSubstantial claim - Findingown data
Algorithmic moderation led to existing financial loss for a YouTuber, decreasing their normal revenue.
“This YouTuber shared their experience of how algorithmic moderation led to existing financial loss, decreasing their normal revenue.”
Quote in the paperSubstantial claim - Findingreasoning
Both YouTubers shared similar experiences of losing progressive ad revenue because of waiting the time required to reverse algorithmic punishments manually.
“Both YouTubers shared similar experiences of losing progressive ad revenue because of waiting the time required to reverse algorithmic punishments manually.”
Quote in the paperSubstantial claim - Findingown data
Part of the algorithmic know-how was to strategically use the appeal process when YouTubers deemed the algorithmic decisions inaccurate.
“Furthermore, part of the algorithmic know-how was to strategically use the appeal process when YouTubers deemed the algorithmic decisions inaccurate.”
Quote in the paperNot a substantial claim - Findingnone given
When punishments happened, their associated explanations seemed to be machine-generated with generic and insufficient language.
“When punishments happened, their associated explanations seemed to be machine-generated with generic and insufficient language.”
Quote in the paperSubstantial claim - Findingnone given
When experiencing algorithmic moderation perceived as inaccurate, YouTubers suspected that algorithmic mechanisms needed more training data (i.e., video content).
“When experiencing algorithmic moderation perceived as inaccurate, YouTubers suspected that algorithmic mechanisms needed more training data (i.e., video content).”
Quote in the paperNot a substantial claim - Findingreasoning
Sometimes, even though being aware of practical knowledge of moderation algorithms, they might fail to circumvent specific future punishments.
“Sometimes, even though being aware of practical knowledge of moderation algorithms, they might fail to circumvent specific future punishments.”
Quote in the paperSubstantial claim - Findingreasoning
They have to engage in long-term learning, especially when they believe that moderation algorithms are also learning and evolving.
“They have to engage in long-term learning, especially when they believe that moderation algorithms are also learning and evolving.”
Quote in the paperSubstantial claim - Findingcitation
Some even organized collective actions to unionize and challenge the platform's governance decisions.
“Some even organized collective actions to unionize and challenge the platform's governance decisions [68].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [68]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingown data
Platforms issuing moderation punishments on content creators have scarcely considered the possibility of audiences' problematic behaviors.
“However, issuing moderation punishments on content creators, platforms have scarcely considered the possibility of audiences' problematic behaviors, as Section 5.3 shown.”
Quote in the paperNot a substantial claim - Findingown data
Our findings highlighted two existing forms of support: peer support and platform support.
“Specifically, our findings highlighted two existing forms of support: peer support and platform support.”
Quote in the paperSubstantial claim - Findingown data
Besides peer support, YouTubers also value support from the platform.
“Besides peer support, YouTubers also value support from the platform.”
Quote in the paperSubstantial claim - Findingown data
In the context of YouTube, YouTubers shared and analyzed their experiences or knowledge to speculate about moderation algorithms.
“In the context of YouTube, we found that YouTubers shared and analyzed their experiences or knowledge to speculate about moderation algorithms.”
Quote in the paperSubstantial claim - Findingown data
YouTubers reported moderation systems repeatedly tagged them with 'limited ads.'
“Supplementing this design, moderation systems should also be aware of videos that already pass moderation since we found YouTubers reported moderation systems repeatedly tagged them with 'limited ads.'”
Quote in the paperSubstantial claim - Findingown data
They are not content with the current level of platform support.
“However, they are not content with the current level of platform support.”
Quote in the paperSubstantial claim - Findingreasoning
YouTubers turn to online communities like our study site for various forms of support.
“While postpunishment transparency work denotes short-term effort to repair, YouTubers turn to online communities like our study site for various forms of support.”
Quote in the paperNot a substantial claim - Findingown data
Peer support is not only for circumventing future moderation.
“Importantly, we uncovered that peer support is not only for circumventing future moderation.”
Quote in the paperSubstantial claim - Findingown data
YouTubers engage in social practices to manage algorithmic precarity.
“Second, YouTubers engage in social practices to manage algorithmic precarity.”
Quote in the paperSubstantial claim - Findingnone given
The opacity of algorithmic moderation, existing at multiple layers, injects more precarity in YouTubers' labor.
“What this study showed is how the opacity of algorithmic moderation, existing at multiple layers, injects more precarity in YouTubers' labor.”
Quote in the paperSubstantial claim - Findingnone given
Our findings involving content policies, human moderators, and YouTubers manifested YouTube's algorithmic decision-making processes have remained confusing to YouTubers.
“Our findings involving content policies, human moderators, and YouTubers manifested YouTube's algorithmic decision-making processes have remained confusing to YouTubers.”
Quote in the paperSubstantial claim - Findingown data
YouTubers jointly expressed emotional support for those who experienced penalties in their precarious work of content creation and sharing.
“YouTubers jointly expressed emotional support for those who experienced penalties in their precarious work of content creation and sharing.”
Quote in the paperSubstantial claim - Findingown data
YouTubers highlighted the importance of direct communication with the platform.
“YouTubers highlighted the importance of direct communication with the platform.”
Quote in the paperSubstantial claim - Findingreasoning
Due to algorithmic precarity and its affective ramifications, emotional support is another category of social support we observed in the community.
“Due to algorithmic precarity and its affective ramifications, emotional support is another category of social support we observed in the community.”
Quote in the paperSubstantial claim - Findingown data
YouTubers become skilled at editing content such as metadata of videos to exert self-moderation.
“Extending prior work discussing how users circumvented moderation on the original social media platforms [16,38], we found YouTubers become skilled at editing content such as metadata of videos to exert self-moderation.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [38]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingcitation
YouTubers sensed various degrees of severity from penalties given their financial dependency on video creation or mental conditions.
“As we described in Section 5.2, YouTubers sensed various degrees of severity from penalties given their financial dependency on video creation or mental conditions.”
Quote in the paperSubstantial claim - Premisecitation
YouTube relies on AI algorithms (e.g., machine learning) in content moderation.
“To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [43]
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- cites [62]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
More YouTubers' livelihoods rely upon the business of making videos on YouTube.
“Nowadays, more YouTubers' livelihoods rely upon the business of making videos on YouTube [2,15,17,50].”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [15]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [17]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [50]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
This information implicitly reflected a lack of a communicative path for YouTubers to contact the YouTube platform for moderation issues directly on YouTube.
“On the other, this information implicitly reflected a lack of a communicative path for YouTubers to contact the YouTube platform for moderation issues directly on YouTube.”
Quote in the paperNot a substantial claim - Premisecitation
Content creation on YouTube is distinct in how it is weaved into the platform economy and manifests as a form of digital labor.
“Thus, while content creation on commonly examined platforms like Reddit and Twitter is usually framed along the line of free expression, content creation on YouTube is distinct in how it is weaved into the platform economy and manifests as a form of digital labor [73,82].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [73]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [82]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
YouTubers utilized the time that an appeal usually took to have enough time to prepare for future moderation.
“Behind this process, YouTubers utilized the time that an appeal usually took to have enough time to prepare for future moderation.”
Quote in the paperNot a substantial claim - Interpretationreasoning
They failed to acquire the turn on their investments of time, energy, and ideas in video creation.
“Thus, they failed to acquire the turn on their investments of time, energy, and ideas in video creation.”
Quote in the paperSubstantial claim - Premisecitation
YouTube's content moderation economically punishes YouTubers, its laborers.
“However, what is less discussed in the literature is the socioeconomic implication of content moderation: YouTube's content moderation economically punishes YouTubers, its laborers.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Rests on an assumption: Relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation.
- Premisecitation
Once a YouTuber is determined to have created harmful content, YouTube demonetizes their user accounts or videos, denying them future ad revenue through limited or no ads.
“Once a YouTuber is determined to have created harmful content, YouTube would demonetize 4 their user accounts or videos, eventually denying them from earning more future ad revenue through limited or no ads placed on videos.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Rests on an assumption: Relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation.
- Interpretationreasoning
Each layer of opacity would require a sizable effort for YouTubers to wrestle with.
“Each layer of opacity would require a sizable effort for YouTubers to wrestle with.”
Quote in the paperSubstantial claim - Interpretationreasoning
The temporality feature of algorithmic moderation reflected the work precarity affecting video content creators' online life to real life.
“This temporality feature of algorithmic moderation reflected the work precarity affecting video content creators' online life to real life.”
Quote in the paperSubstantial claim - Interpretationown data
YouTubers were empowered by algorithmic precarity to exert the autonomy of stabilizing their income.
“However, experiencing all these aspects of algorithmic precarity, YouTubers also showed how they were empowered by such precarity to exert the autonomy of stabilizing their income.”
Quote in the paperSubstantial claim - Interpretationreasoning
Limited transparency in algorithmic moderation made it implausible for YouTubers to confirm or evaluate differential treatments, causing confusion.
“These examples here reflected that limited transparency in algorithmic moderation caused it implausible for YouTubers to confirm or evaluate differential treatments but with confusion.”
Quote in the paperSubstantial claim - Interpretationreasoning
Algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity.
“Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity.”
Quote in the paperSubstantial claim - Interpretationreasoning
YouTube's algorithmic moderation has resulted in more work from YouTubers.
“YouTube's algorithmic moderation has resulted in more work from YouTubers.”
Quote in the paperSubstantial claim - Interpretationreasoning
YouTubers having algorithmic knowledge could provide reflective suggestions for moderation systems' design even though they are in the hardship of avoiding future moderation risks.
“This example further showed that YouTubers having algorithmic knowledge could provide reflective suggestions for moderation systems' design even though they are in the hardship of avoiding future moderation risks.”
Quote in the paperSubstantial claim - Interpretationown data
Algorithmic demonetization imposes consequential impacts on YouTubers' real life.
“Furthermore, algorithmic demonetization would further impose consequential impacts on YouTubers' real life.”
Quote in the paperSubstantial claim - Premisecitation
As YouTube moderation develops complex policies and enforcement strategies, its opacity grows.
“As YouTube moderation develops complex policies and enforcement strategies [13], its opacity grows.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
YouTube moderation's algorithmic penalties and the corresponding socioeconomic impacts rendered YouTubers' work precarity.
“Hence, YouTube moderation's algorithmic penalties and the corresponding socioeconomic impacts rendered YouTubers' work precarity.”
Quote in the paperSubstantial claim - Interpretationreasoning
Both YouTubers had a high tolerance towards algorithmic moderation.
“This instance reflected that both YouTubers had a high tolerance towards algorithmic moderation.”
Quote in the paperNot a substantial claim - Interpretationcitation
This example indicated a practical level of self-moderation behaviors on YouTube.
“Resonating with self-moderation on social media [85], this example indicated a practical level of self-moderation behaviors on YouTube.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [85]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationown data
The opacity of moderation algorithms had intensified the precarity of YouTubers' content creation work.
“In particular, our study showed how the opacity of moderation algorithms had intensified the precarity of YouTubers' content creation work.”
Quote in the paperSubstantial claim - Interpretationreasoning
Peer and platform support could serve as a restorative justice means.
“We then showed how peer and platform support could serve as a restorative justice means.”
Quote in the paperSubstantial claim - Interpretationreasoning
This action overcame the limitation of an appeal process that YouTube allows YouTubers to do only once.
“On the one hand, this action overcame the limitation of an appeal process that YouTube allows YouTubers to do only once.”
Quote in the paperNot a substantial claim - Interpretationreasoning
It further showed the process of collective sensemaking: how YouTubers collaborated to piece their past experiences together and speculated about how moderation algorithms worked.
“It further showed the process of collective sensemaking: how YouTubers collaborated to piece their past experiences together and speculated about how moderation algorithms worked.”
Quote in the paperNot a substantial claim - Interpretationreasoning
In the case of YouTube moderation, the opacity has multiple layers.
“In the case of YouTube moderation, the opacity has multiple layers.”
Quote in the paperSubstantial claim - Interpretationown data
Our study extended Gillespie's work that unevenness not only appeared in distributed human moderation but also in automated moderation, as our case of YouTube's demonetization penalties shown.
“Also, our study extended Gillespie's work that unevenness not only appeared in distributed human moderation [39] but also in automated moderation, as our case of YouTube's demonetization penalties shown.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [39]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
There is an affective dimension to precarity management.
“Furthermore, there is an affective dimension to precarity management.”
Quote in the paperNot a substantial claim - Interpretationcitation
When platform policies are unclear and enforced unevenly, algorithmic know-how becomes important situated knowledge in algorithmically mediated work.
“When platform policies are unclear and enforced unevenly [13], algorithmic know-how becomes important situated knowledge in algorithmically mediated work.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Prior studies largely investigated how users who mainly generate text content interact with moderation systems on social media such as Reddit, Instagram, and Facebook.
“On the one hand, prior studies largely investigated how users who mainly generate text content interact with moderation systems on social media such as Reddit [48,52], Instagram [16,30], and Facebook [66,93].”
Quote in the paperSubstantial claim0 of 6 sources checked- cites [48]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [52]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [66]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [93]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
This study indirectly extended the concept of precariousness mainly discussed around sharing economy and digital worksites.
“On the other, this study indirectly extended the concept of precariousness mainly discussed around sharing economy and digital worksites (e.g., [60,64,76,79]).”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [60]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [64]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [76]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [79]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
The socioeconomic punishment on YouTube could be considered as qualitatively more severe than free expression platforms such as Twitter and Reddit: punishment comes with not just the deprivation of the privilege of expression but also the deprivation of revenue.
“Due to this, the socioeconomic punishment on YouTube could be considered as qualitatively more severe than free expression platforms such as Twitter and Reddit: punishment comes with not just the deprivation of the privilege of expression but also the deprivation of revenue.”
Quote in the paperSubstantial claim - Study design and conductown data
The dataset does not include information about YouTuber categories (e.g., games, lifestyle, music).
“Also, the dataset does not include information about YouTuber categories (e.g., games, lifestyle, music).”
Quote in the paperSubstantial claim - Study design and conductnone given
This research only collected data that explicitly discussed YouTube's content moderation based on relevant literature and media reports.
“This research only collected the data that explicitly discussed YouTube's content moderation based on relevant literature and media reports.”
Quote in the paperSubstantial claim - Interpretationreasoning
YouTubers' algorithmic labor involves striking a delicate balance between enhancing visibility on the one hand and avoiding moderation algorithms on the other.
“As such, YouTubers' algorithmic labor involves striking a delicate balance between enhancing visibility on the one hand and avoiding moderation algorithms on the other.”
Quote in the paperSubstantial claim - Premisecitation
Users might rush to make conclusions based on personal experiences.
“Resonating with prior evidence that users might rush to make conclusions based on personal experiences [87], we stress the importance of providing educational explanations to match with various YouTubers' backgrounds by the algorithmic moderation system.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [87]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
YouTube has high (>95%) dependence on automatic moderation.
“Besides, we did not discuss human moderation (i.e., initially manual flag or moderation) on YouTube due to YouTube's high (>95%) dependence on automatic moderation [42].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [42]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
Part of YouTubers' work is to manage such algorithmic precarity.
“Thus, part of YouTubers' work is to manage such algorithmic precarity.”
Quote in the paperSubstantial claim - Study design and conductreasoning
The subreddit data is not less valuable despite possible self-selection.
“Also, it is possible that people who find moderation punishments to be fair don't come to the subreddit to complain, but this doesn't make the subreddit data less valuable.”
Quote in the paperNot a substantial claim - Study design and conductown data
In the analysis process, the authors cannot unearth whether differences of experiences or post-punishment interactions with moderation would exist between specific YouTuber types.
“As such, in the analysis process, we cannot unearth whether differences of experiences or post-punishment interactions with moderation would exist between specific YouTuber types.”
Quote in the paperNot a substantial claim - Interpretationreasoning
When YouTube cannot provide convincing explanations for true-positive problematic content classification, YouTubers cannot learn from their past behaviors.
“So, even though YouTubers' content is classified as true-positive problematic, when YouTube cannot provide convincing explanations, YouTubers cannot learn from their past behaviors.”
Quote in the paperSubstantial claim
- It may state two separate claims as one. 44
- Probably not a substantial claim. 75
- The quote only partly states this claim. 3
It may state two separate claims as one.
Findingown dataThe appeal result was delivered within a short time, which conflicted with the YouTuber's shared sense of understanding of the appeal process.
“However, the appeal result was delivered within a short time, which conflicted with the YouTuber's shared sense of understanding of the appeal process.”
Quote in the paperSubstantial claimProbably not a substantial claim. It may state two separate claims as one. The quote only partly states this claim.
Findingown dataA YouTuber quoted an email saying their account was terminated due to repeated abusive, hateful, and/or harassing comments that violate community guidelines, and described it as 'Stupidly vague'; they said everyone gets the same message, YouTube does not tell which post or posts were reported and how they violated guidelines, and at least could screenshot the post but did nothing.
“Your account has been terminated as a result of repeated abusive, hateful, and/or harassing comments that violate our community guidelines.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Findingown dataYouTuber D expressed the confusion of how YouTube's algorithms made moderation decisions without reviewing the stream content.
“YouTuber D expressed the confusion of how YouTube's algorithms made moderation decisions without reviewing the stream content.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Findingown dataA YouTuber described their action of avoiding potential financial loss by posting live streaming schedules earlier.
“In this quote, the YouTuber described their action of avoiding potential financial loss by posting live streaming schedules earlier.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
FindingreasoningThe YouTuber called for verified audiences under the assumption that non-qualified audiences can still watch videos for their monetization.
“Last, the YouTuber here called for verified audiences under the assumption that non-qualified audiences can still watch videos for their monetization.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Findingown dataBoth YouTubers expressed emotional support and recommended the punished YouTuber to consistently reverse algorithmic penalties.
“Also, they both expressed emotional support and recommended the punished YouTuber to consistently reverse algorithmic penalties.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper may give no reason why it holds.
Findingnone givenEven if the adjudication of a moderation case is clear-cut to most people, the YouTuber who received the penalty could experience it differently and feel confused.
“Even if the adjudication of a moderation case is clear-cut to most people, the YouTuber who received the penalty could experience it differently and feel confused.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Findingown dataPunishments were oftentimes issued irregularly on the same video content or disproportionally on different YouTubers due to their scale of the fan base.
“Besides striving to create popular content, they also have to be mindful of the inconsistency of moderation algorithms, where punishments were oftentimes issued irregularly on the same video content or disproportionally on different YouTubers due to their scale of the fan base.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Findingown dataYouTuber G explicitly explained the reason why the bot makes demonetization decisions and highlighted the expectation that more appeals can help the algorithms be trained better, which was encouraged by YouTuber G to repair past penalties.
“YouTuber G explicitly explained the reason why the bot makes demonetization decisions and highlighted the expectation that more appeals can help the algorithms be trained better, which was encouraged by YouTuber G to repair past penalties.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
Findingnone givenThe perceived unevenness in algorithmic moderation resonated with many media reports.
“The example here might involve personally uneven sentiment; however, this perceived unevenness also resonated with many media reports.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Findingown dataA viewer who did not post videos or comments and watched makeup, clothing hauls, music, and random video game had their account suspended, and could not associate any prior behavior with the punishment.
“I do not post videos.I do not comment on anything.I watch makeup videos, clothing hauls, music, and random video game.There are zero reasons I should have my account suspended.I would love to know because I literally just watch videos.”
Quote in the paperNot a substantial claimProbably not a substantial claim.
Findingown dataThe analysis distilled three high-level themes: 'Being Confused in Algorithmic Opacity', 'Managing Algorithmic Precarity', and 'Learning and Applying Algorithmic Know-How'.
“We integrated and distilled into three high-level themes for the research question in the repetitive rounds of coding.These themes included”
Quote in the paperSubstantial claim- Rests on an assumption: There is a lack of systematic investigations on how YouTubers perceive and learn from algorithmic moderation's decisions and how they handle demonetization.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
Findingown dataThe YouTuber envisioned a filter to manage the audience's comments on their videos.
“In this example, the YouTuber envisioned a filter to manage the audience's comments on their videos.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Findingown dataA YouTuber's channel with 20,000 subs was suspended; they filed an appeal but received an automated email response stating that they decided to uphold the ban after only reviewing for 3 minutes.
“My channel with 20,000 subs being suspended.I have since filed an appeal but received an automated email response stating that they have decided to uphold the ban after only reviewing for 3 minutes.”
Quote in the paperNot a substantial claimProbably not a substantial claim.
Findingown dataSome YouTubers claimed they experienced uneven punishments compared to other YouTubers, especially those with larger subscriber bases.
“Some YouTubers claimed they experienced uneven punishments than other YouTubers, especially those largely subscribed by viewers.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataBoth YouTubers showed empathy for the punished YouTubers and encouraged them to reverse the issued punishments consistently.
“Both YouTubers showed empathy for the punished YouTubers and encouraged them to reverse the issued punishments consistently.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Findingown dataYouTubers shared and analyzed their punishment experiences to speculate about moderation algorithms, which in turn informed operations of repairing their past moderation punishments and avoiding future interactions with YouTube moderation.
“Specifically, YouTubers shared and analyzed their punishment experiences to speculate about moderation algorithms, which in turn informed operations of repairing their past moderation punishments and avoiding future interactions with YouTube moderation.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Findingown dataA YouTuber had a flagged video suddenly go back to being monetized, then within an hour or two it was back to being flagged again; the YouTuber was unsure if it was a sign of the bot learning and re-flagging or a glitch.
“I did have one of my flagged videos suddenly go back to being monetized last week, but within an hour or two, it was back to being flagged again.So, I am not sure if that was a sign of the bot learning and then re-flagging for some other reason or if it was just a slight glitch with the site.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
Findingnone givenThey described the moderation experience by claiming how YouTube rarely showed considerations for YouTubers.
“They described the moderation experience by claiming how YouTube rarely showed considerations for YouTubers.”
Quote in the paperNot a substantial claimProbably not a substantial claim. It may state two separate claims as one.
Findingown dataA YouTuber's channel was automatically suspended without any warning; they received a first email with no explanation; after sending another email, they were told it was based on community guidelines; they reviewed the guidelines and did not violate them; they received no strikes or warnings like promised.
“My channel has been automatically suspended without any warning.I received the first email with no explanation.Then after I sent another email, they said it was based on community guidelines.I reviewed the guidelines, and I did not violate any of those things.If I did, I received no strikes or warnings like they promised to do.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Findingown dataYouTuber F described their demonetization experiences as well as the appeal's outcomes.
“YouTuber F, in detail, described their demonetization experiences as well as the appeal's outcomes.”
Quote in the paperNot a substantial claimProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
Findingnone givenThe causal logic contained in this example resonated well with many other YouTubers' stated experiences.
“Even though we cannot verify this YouTuber's account information, the causal logic contained in this example resonated well with many other YouTubers' stated experiences.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Findingown dataYouTubers pointed to how their feedback in the form of appeal could help improve moderation algorithms.
“Reflexively, YouTubers pointed to how their feedback in the form of appeal could help improve moderation algorithms.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
FindingreasoningYouTube demonetized a YouTuber at a channel level.
“This case showed how YouTube demonetized a YouTuber at a channel level.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Findingown dataA YouTuber encountered anxiety and felt less motivated for future video creation, implicitly indicating negative emotions toward YouTube moderation.
“By stating a precise amount of revenue deduction, this YouTuber encountered anxiety and felt less motivated for future video creation, which implicitly indicated their negative emotions toward YouTube moderation.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
Findingnone givenA YouTuber mentioned their desire for completely transferring to an alternative video streaming platform of YouTube, namely Bitchute, to seek lower moderation.
“This YouTuber mentioned their desire for completely transferring to an alternative video streaming platform of YouTube, namely Bitchute, to seek lower moderation.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Findingown dataYouTuber C provided personal experiences of how to cope with moderation penalties.
“YouTuber C provided personal experiences of how to cope with moderation penalties.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Findingown dataThe YouTube platform did not specify which part of the video violated content policies; its explanations were generic and not situated in specific cases.
“Here, the YouTube platform did not specify which part of the video violated content policies.Instead, its explanations were generic and not situated in specific cases.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Findingown dataThe YouTuber claimed that they received the email that seemed to be an automated response.
“The YouTuber here claimed that they received the email that seemed to be an automated response.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
FindingreasoningThis irregularity caused the YouTuber to speculate about how algorithms worked behind automated penalties.
“This irregularity caused the YouTuber to speculate about how algorithms worked behind automated penalties.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Findingown dataYouTube E stated that metadata of videos helped the bot make algorithmic predictions/classifications.
“YouTube E stated that metadata of videos helped the bot make algorithmic predictions/classifications.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
FindingreasoningThis case showed the confusion and dissatisfaction caused by the opaque moderation explanations.
“While the YouTuber here did not disclose the email details, this case showed the confusion and dissatisfaction caused by the opaque moderation explanations.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
FindingcitationYouTubers switched between or migrated to other streaming platforms to seek a lower degree of future moderation risks, different from prior work uncovering that users stayed at the same platform to do so.
“Besides YouTubers showing how they coped with the demonetization and its socioeconomic impacts, they also switched between or migrated to other streaming platforms to seek a lower degree of future moderation risks, different from prior work uncovering that users stayed at the same platform to do so [16].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
FindingreasoningThe YouTuber believed that bots needed time to learn from video data but still expressed confusion on algorithmic decision-making over time.
“This example showed the YouTuber believed that bots needed time to learn from video data but still expressed their confusion on algorithmic decision-making over time.”
Quote in the paperNot a substantial claimProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
Findingnone givenYouTube was found to have similar problems.
“YouTube was found to have similar problems.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Findingown dataWe found many YouTubers with all past nonproblematic records of videos but suddenly encountered algorithmic penalties, rendering an inconsistent moderation procedure.
“We found many YouTubers with all past nonproblematic records of videos but suddenly encountered algorithmic penalties, rendering an inconsistent moderation procedure.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
FindingreasoningYouTubers would experience intense negative emotions upon demonetization punishments because these decisions are of high stakes and also because these decisions bring high uncertainty and opacity.
“YouTubers would experience intense negative emotions upon demonetization punishments because these decisions are of high stakes and also because these decisions bring high uncertainty and opacity.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Findingown dataThey use different scheduling to work with the delay of demonetization (future moderation) as well as consistently and strategically appeal to reinstate monetization status (past moderation).
“They use different scheduling to work with the delay of demonetization (future moderation) as well as consistently and strategically appeal to reinstate monetization status (past moderation).”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingnone givenThey turned to external communication platforms seeking informational support and proactively bridged the communication with YouTube.
“They turned to external communication platforms seeking informational support and proactively bridged the communication with YouTube.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataYouTubers have to both repair their past moderation punishments and avoid future moderation risks.
“First, YouTubers have to both repair their past moderation punishments and avoid future moderation risks.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingnone givenEven though reversing those penalties ultimately, they took the economic loss that happened at that time.
“Even though reversing those penalties ultimately, they took the economic loss that happened at that time.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataYouTubers in our study were unsatisfied with their communication with the platform and voiced complaints about the lack of transparency even within the appeal process.
“YouTubers in our study were unsatisfied with their communication with the platform and voiced complaints about the lack of transparency even within the appeal process.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
FindingreasoningThey utilize online communities to constantly update their algorithmic know-how as a preemptive strategy, as well as remain reflexive.
“They utilize online communities to constantly update their algorithmic know-how as a preemptive strategy, as well as remain reflexive.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataYouTubers, as content creators, were found to both repair past moderation punishments and avoid future moderation risks.
“However, YouTubers, as content creators, were found to both repair past moderation punishments and avoid future moderation risks.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataYouTubers actively learned from algorithmic penalties and developed knowledge of moderation algorithms collectively.
“By extending these studies, we found YouTubers actively learned from algorithmic penalties and developed knowledge of moderation algorithms collectively.”
Quote in the paperSubstantial claimProbably not a substantial claim.
Findingown dataOur findings pointed to several other dimensions of managing algorithmic precarity.
“Besides the financial strategy, our findings pointed to several other dimensions of managing algorithmic precarity.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper may give no reason why it holds.
Premisenone givenYouTube follows a retributive justice logic to punish convicted YouTubers through demonetization.
“To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
PremisecitationVideo creation is time-consuming; YouTubers could spend 80 hours a week creating and editing videos.
“Video creation is time-consuming; YouTubers could spend 80 hours a week creating and editing videos [2].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
InterpretationreasoningEven temporary demonetization could incur a substantial financial loss even though they could reverse the demonetization ultimately.
“Thus, even temporary demonetization could incur a substantial financial loss even though they could reverse the demonetization ultimately.”
Quote in the paperSubstantial claim- Rests on an assumption: Video content is oftentimes time-sensitive.
Probably not a substantial claim. It may be too vague to check against evidence.
InterpretationreasoningYouTubers must do more work to restore transparency to their punishments.
“Subsequently, YouTubers must do more work to restore transparency to their punishments.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one. The quote only partly states this claim.
Study design and conductreasoningWhen presenting findings, the authors paraphrased each quote to decrease its searchability and used the singular 'they' pronoun to interpret data, further assuring the dataset's anonymity.
“When presenting the findings, we paraphrased each quote to decrease its searchability on Redditt and used the singular”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Study design and conductreasoningThe authors removed user information such as usernames and URLs, so the dataset did not contain any personally identifiable information.
“We removed user information such as usernames and the URLs of posts, so the dataset did not contain any personally identifiable information.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationThis dialogue resonated with Caplan's findings that the video's metadata might trigger YouTube moderation.
“This dialogue resonated with Caplan's findings [13] that the video's metadata might trigger YouTube moderation.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
InterpretationreasoningThey further developed the prior knowledge from the bot algorithmically classifying content based on metadata to how the bot made final decisions for issuing penalties.
“Besides, they further developed the prior knowledge from the bot algorithmically classifying content based on metadata to how the bot made final decisions for issuing penalties.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Study design and conductnone givenThe study was approved by the university's Institutional Review Board (IRB).
“After our university's Institutional Review Board (IRB) approved this study, we performed data collection and analysis.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
InterpretationreasoningThe YouTuber expected algorithms to be consistent with the time dimension where each video was supposed to experience a one-time algorithmic inspection.
“In the above case, the YouTuber might expect the algorithms to be consistent with the time dimension where each video was supposed to experience a one-time algorithmic inspection.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
InterpretationreasoningBoth examples regarding tweaking metadata further pointed out the labor of dealing with moderation punishments.
“Also, both examples regarding tweaking metadata further pointed out the labor of dealing with moderation punishments.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Study design and conductown dataThe final dataset contained 3,086 threads and 36,279 comments.
“The final dataset contained 3,086 threads and the associated 36,279 comments.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
InterpretationreasoningYouTubers shifting between platforms would encounter challenges for acquiring more future economic benefits due to the lower fanbase, incomparable to what they can reach on YouTube.
“Hence, YouTubers shifting between platforms would encounter challenges for acquiring more future economic benefits due to the lower fanbase, incomparable to what they can reach on YouTube.”
Quote in the paperNot a substantial claim- Rests on an assumption: YouTube is currently the largest video streaming platform, and competitors hardly have more userbase and daily engagement data than YouTube.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
Study design and conductown dataThe comment date ranged from June 8, 2012, to December 23, 2020, and the average number of comments per thread was around 21.82.
“In detail, the comment date ranged from June 8, 2012, to December 23, 2020, and the average number of comments of each thread was around 21.82.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
InterpretationreasoningThis dialogue showed both YouTubers' desire for a moderation system with better perceived accuracy.
“This dialogue here showed both YouTubers' desire for a moderation system with better perceived accuracy.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationSuch inconsistency resonated with Vaccaro et al.'s investigations on Facebook, where policies were found to be applied on users at uneven levels.
“Such inconsistency resonated with Vaccaro et al.'s investigations on Facebook, where policies were found to be applied on users at uneven levels [95].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [95]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
InterpretationreasoningWith feeling a lack of agency, they had hope for a better moderation system.
“With feeling a lack of agency, they had hope for a better moderation system.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationAs cultural production moves onto platforms, communication scholars have observed how algorithms exacerbate the instability of such work.
“As cultural production moves onto platforms, communication scholars have observed how algorithms exacerbate the instability of such work [29].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
Study design and conductcitationData collection continued until the dataset became saturated.
“We continued this process to add additional keywords to iteratively search more threads until the dataset became saturated [11], indicating there would be no new information provided.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
InterpretationreasoningYouTubers actively coped with future financial loss to both exert their autonomy and alleviate their work uncertainty.
“Here, the discussions and behaviors about using external funding platforms indicated that YouTubers actively coped with the future financial loss to both exert their autonomy and alleviate their work uncertainty.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Interpretationown dataContacting outside entities reflected the YouTuber actively coped with penalties and pointed out the generally limited communicative methods for YouTubers to solve their economic problems with the YouTube platform.
“Contacting outside entities reflected the YouTuber actively coped with penalties and pointed out the generally limited communicative methods for YouTubers to solve their economic problems with the YouTube platform.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationPrevious research has confirmed the importance of human moderators in providing moderation explanations.
“Previous research has confirmed the importance of human moderators in providing moderation explanations [47,101].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [47]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [101]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
InterpretationreasoningAlgorithms mediate the work of video creation and cause uncertainty to work as a video content creator.
“This direct economic punishment could also indicate that algorithms mediate the work of video creation and cause uncertainty to work as a video content creator.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationPrior work has discussed how social media users typically encountered algorithmic moderation's inexplicability.
“Prior work has discussed how social media users typically encountered algorithmic moderation's inexplicability (e.g., [33,47,52,74]).”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [47]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [52]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [74]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
PremisecitationPrevious research discussed that social media users hardly felt a sense of agency in algorithmic moderation systems and how they took actions to strengthen their agency.
“Previous research discussed that social media users hardly felt a sense of agency in algorithmic moderation systems [32] and how they took actions to strengthen their agency [66].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [66]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
PremisecitationPrevious research uncovered that users could hardly communicate with the platform for moderation issues and called for a more communicative process in moderation systems.
“Previous research uncovered that users could hardly communicate with the platform for moderation issues [46] and called for a more communicative process in moderation systems [93].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [46]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [93]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. It may state two separate claims as one.
InterpretationreasoningWe extended these studies by focusing on video content creators' perspectives and stressed the increased precarity due to socioeconomic punishments: demonetization, its impacts, and the labor of handling moderation punishments.
“We extended these studies by focusing on video content creators' perspectives and stressed the increased precarity due to socioeconomic punishments: demonetization, its impacts, and the labor of handling moderation punishments.”
Quote in the paperSubstantial claimProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
PremisecitationA restorative justice lens implies that we should also value offenders' experiences and seek to repair harm and reintegrate offenders back into the community.
“A restorative justice lens implies that we should also value offenders' experiences and seek to repair harm and reintegrate offenders back into the community [6].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
Interpretationnone givenThe work precarity resulting from demonetization punishments also has transferable meanings.
“Lastly, the work precarity resulting from demonetization punishments also has transferable meanings.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
Study design and conductnone givenIt is possible that people who find moderation punishments to be fair don't come to the subreddit to complain.
“Also, it is possible that people who find moderation punishments to be fair don't come to the subreddit to complain, but this doesn't make the subreddit data less valuable.”
Quote in the paperNot a substantial claimProbably not a substantial claim. It may be too vague to check against evidence.
InterpretationreasoningYouTubers are in need of peer and platform support that could come in a sufficient and efficient way.
“They are in need of peer and platform support that could come in a sufficient and efficient way.”
Quote in the paperNot a substantial claimProbably not a substantial claim. It may be too vague to check against evidence.
InterpretationreasoningOur study provided a detailed account of how moderation algorithms are also intertwined with the already fraught labor relation of content creation.
“In this regard, our study provided a detailed account of how moderation algorithms are also intertwined with the already fraught labor relation of content creation.”
Quote in the paperNot a substantial claimProbably not a substantial claim. It may be too vague to check against evidence.
InterpretationreasoningWe extend this by highlighting how peer and platform support could also contribute to offenders' behavioral improvement.
“We extend this by highlighting how peer and platform support could also contribute to offenders' behavioral improvement.”
Quote in the paperNot a substantial claim- Rests on an assumption: When offenders do not comprehend the penal rationales, punishments alone could hardly reform offenders.
Probably not a substantial claim.
Study design and conductreasoningImplicit expressions that do not contain relevant keywords are hard to identify.
“Hence, those implicit expressions that do not contain relevant keywords are hard to identify.”
Quote in the paperSubstantial claimProbably not a substantial claim. It may be too vague to check against evidence.
InterpretationreasoningThe subreddit data provides a window in which frustrations lie.
“Rather, it provides a window in which frustrations lie and from where design could bring consistent punishment experiences not to be frustrating.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
PremisecitationPeople could experience anxiety, loss of agency, and negative emotions when dealing with complex algorithmic systems.
“People could experience anxiety, loss of agency, and negative emotions when dealing with complex algorithmic systems [12,49].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [12]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [49]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
InterpretationreasoningWe should note the precarity caused by socioeconomic content moderation and how creators manage such precarity, just as the case of YouTubers shown.
“At the same time, we should note the precarity caused by socioeconomic content moderation and how creators manage such precarity, just as the case of YouTubers shown.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationResearchers in the CSCW community have previously used specific subreddits' discussion data for different research topics.
“Researchers in the CSCW community have previously used specific subreddits' discussion data for different research topics (e.g., [5,80]).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [80]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. It may be too vague to check against evidence.
InterpretationreasoningThe bias will last.
“Thus, the bias will last.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationContent creation involves monetization and its connections with audiences (e.g., crowdfunding from subscribers on Twitch).
“We can see that content creation involves monetization and its connections with audiences (e.g., crowdfunding from subscribers on Twitch [51,71]).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [51]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [71]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationPrevious scholarship has reported how YouTubers have to consider financial alternatives to ad revenue from YouTube.
“Previous scholarship has reported how YouTubers have to consider financial alternatives to ad revenue from YouTube [4,13].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [13]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationWest proposed that content moderation systems could be more educational.
“West proposed that content moderation systems could be more educational [66].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [66]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationEven an ideal machine learning model cannot reach 100% test accuracy due to various reasons (e.g., overfitting).
“Even an ideal machine learning model cannot reach 100% test accuracy due to various reasons (e.g., overfitting) [24].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [24]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
It may state two separate claims as one.
InterpretationreasoningBoth peer support and platform support represent a meaningful departure from retributive justice's simplistic logic of using penalties to fix offenders.
“Both peer support and platform support represent a meaningful departure from retributive justice's simplistic logic of using penalties to fix offenders.”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Study design and conductreasoningThe authors can still systematically distill meaningful experiential conclusions from the subreddit data.
“However, we can still systematically distill meaningful experiential conclusions from the data and point out the urgent importance of designing more transparent algorithmic moderation to mitigate such bias.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationPrevious researchers have called for democratic accountability, transparency, and free expression from the internet platforms.
“Previous researchers have called for democratic accountability, transparency, and free expression from the internet platforms [39,48,66].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [39]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [48]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [66]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim.
PremisecitationPrior studies have investigated how punished users supported each other to bypass future moderation on social media.
“Prior studies have investigated how punished users supported each other to bypass future moderation on social media [16,38].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [38]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. It may be too vague to check against evidence.
InterpretationreasoningPost-punishment support could serve as a restorative justice means.
“Relatedly, post-punishment support could serve as a restorative justice means.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Study design and conductcitationThe authors did not discuss human moderation on YouTube.
“Besides, we did not discuss human moderation (i.e., initially manual flag or moderation) on YouTube due to YouTube's high (>95%) dependence on automatic moderation [42].”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Study design and conductnone givenUsing subreddit discussion data could bring bias, where YouTubers might share biased or conflicting information.
“We acknowledge using this type of data could be possible to bring bias, where YouTubers might share biased or conflicting information.”
Quote in the paperNot a substantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
PremisecitationIn HCI and CSCW, researchers have reflected upon the limits of retributive justice, the punitive system that YouTube currently employs, and paid more attention to restorative justice.
“In HCI and CSCW, researchers have reflected upon the limits of retributive justice, the punitive system that YouTube currently employs, and paid more attention to restorative justice [6,81].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [81]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationPrior studies discussed peer support on social media.
“Prior studies discussed peer support on social media [58,61,102].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [58]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [61]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [102]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
The paper relies on this without support.
Relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation.
“Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation.”
“Currently, little attention has been paid to understand YouTubers' socioeconomic interactions with algorithmic moderation or, in other words, how YouTubers interact with the socioeconomic punishments (i.e., demonetization) caused by YouTube moderation.”
The paper relies on this without support.
There is a lack of systematic investigations on how YouTubers perceive and learn from algorithmic moderation's decisions and how they handle demonetization.
“However, by standing at the (video) content creator's perspective, there is still a lack of systematic investigations on how YouTubers perceive and learn from the algorithmic moderation's decisions as well as how they handle the socioeconomic punishment, namely demonetization.”
The paper relies on this without support.
r/youtube is the largest online community having more members than any other YouTube-related forums such as yttalk.
“It is the largest online community having more members than any other YouTube-related forums such as yttalk 12 .”
The paper relies on this without support.
Video content is oftentimes time-sensitive.
“Plus, video content is oftentimes time-sensitive.”
The paper relies on this without support.
YouTube is currently the largest video streaming platform, and competitors hardly have more userbase and daily engagement data than YouTube.
“Since YouTube is currently the largest video streaming platform, competitors hardly have more userbase and daily engagement data than YouTube.”
The paper relies on this without support.
When offenders do not comprehend the penal rationales, punishments alone could hardly reform offenders.
“When offenders do not comprehend the penal rationales, punishments alone could hardly reform offenders.”
The paper relies on this without support.
Platforms such as Facebook, Twitter, and YouTube benefit from business interests of providing audience management functions such as 'customize audience' to gain more income.
“Also, platforms such as Facebook, Twitter, and YouTube benefit from business interests of providing audience management functions such as 'customize audience' to gain more income.”
The paper relies on this without support.
Moderation plays a profound role in dictating some's livelihoods.
“moderation plays a profound role in dictating some's livelihoods.”
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Metadata
- Authors
- Ma, R., & Kou, Y.
- Affiliations
- Pennsylvania State University (US)
- ORCID
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2021-10-13
- Volume
- 5
- Issue
- CSCW2
- Pages
- 1-25
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W3205745750
- Cited by
- 102
- References
- 83
- Citations by year
- 2026: 27 · 2025: 23 · 2024: 20 · 2023: 23 · 2022: 9
- FWCI
- 3.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion)
- ScholarSphere (Penn State Libraries) (submittedVersion)
- Funders
- NSF (2006854)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- automated content moderation, YouTube, YouTubers, demonetization, algorithmic labor, algorithmic opacity, precarious employment, retributive justice, restorative justice, practical knowledge, socio-economic impacts
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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