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Big Questions for Social Media Big Data: Representativeness, Validity and Other Methodological Pitfalls
Open accessAbstract
Large-scale databases of human activity in social media have captured scientific and policy attention, producing a flood of research and discussion. This paper considers methodological and conceptual challenges for this emergent field, with special attention to the validity and representativeness of social media big data analyses. Persistent issues include the over-emphasis of a single platform, Twitter, sampling biases arising from selection by hashtags, and vague and unrepresentative sampling frames. The socio-cultural complexity of user behavior aimed at algorithmic invisibility (such as subtweeting, mock-retweeting, use of “screen captures” for text, etc.) further complicate interpretation of big data social media. Other challenges include accounting for field effects, i.e. broadly consequential events that do not diffuse only through the network under study but affect the whole society. The application of network methods from other fields to the study of human social activity may not always be appropriate. The paper concludes with a call to action on practical steps to improve our analytic capacity in this promising, rapidly-growing field.
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“Social data opens unprecedented opportunities to answer significant questions about society, policies, and health, being recognized as one core reason behind progress in many areas of computing (e.g., crisis informatics, digital health, computational social science) (Crawford and Finn, 2014; Tufekci, 2014; Yom-Tov, 2016).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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In the citing paper
“The behaviors we observe in online social platforms are partially determined by platform capabilities, which are engineered toward certain goals”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The functional peculiarities of social platforms may introduce population (section 3.2) and behavioral biases (section 3.3) by influencing which user demographics are drawn to each platform and the kind of actions they are more likely to perform”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Overlooking the impact of norms can impact any social data analysis studying or making assumptions about user behavior”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“if the data is organized along a certain attribute (e.g., the presence of a keyword or hashtag), and there are multiple independent factors that result in the attribute taking a certain value, analyzing data entries with this value is equivalent to conditioning on it, and may result in spurious patterns of association among these factors (Blyth, 1972; Tufekci, 2014).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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“Yet, such implications are not always well understood or recognized (Tufekci, 2014; O'Neil, 2016), as many seem to assume that these data, and the frameworks used to handle them, are adequate, often as-is, for the problem at hand, with little or no scrutiny.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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“Regardless of how large or varied social data are, there are also lingering questions about what can be learned from them about real-world phenomena (online or offline)—which have yet to be rigorously addressed (boyd and Crawford, 2012; Ruths and Pfeffer, 2014; Tufekci, 2014).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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“A key concern is that research agendas tend to be opportunistically driven by access to data, tools, or ease of analysis (Ruths and Pfeffer, 2014; Tufekci, 2014; Weller and Gorman, 2015); or, as Baeza-Yates (2013) puts it, “we see a lot of data mining for the sake of it.””
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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“much research is concentrated on data from a few platforms, most notably Twitter.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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“Functional biases make conclusions from research studies difficult to generalize or transfer, as each platform exhibits its own structural differences (Tufekci, 2014), which can lead to platform-specific phenomena (Ruths and Pfeffer, 2014) that are often overlooked.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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In the citing paper
“For instance, we often know what people write, but not what they read, and we may know who clicked on or “liked” something, but not who read it or watched it”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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“The choice of keywords in keyword-based queries shapes the resulting datasets.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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“different hashtags used in the same context (e.g., during a political event) may be associated with distinct social, political or cultural frames, and, thus, samples built using them may embed different dimensions of the data (Tufekci, 2014)”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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“Many datasets are built around dependent variables (see section 5.2), deciding to include a user or a piece of content depending on the inclusion of a variable under analysis (Tufekci, 2014); this may result in apparent patterns of association that fail to generalize.”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
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In the citing paper
“The same mechanism or process may capture different signals across contexts (Tufekci, 2014), but such distinctions are often unintelligible and hard to make in an automated fashion when looking at a system output or at an analysis results in aggregate (Rost et al., 2013; Tufekci, 2014).”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Tufekci, 2014
In the citing paper
“For instance, if a study investigates the effectiveness of a particular treatment based on data collected from a specific time period, the findings may not reflect the current healthcare landscape or advancements in medical practices [91].”
From Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond (2024) · cites this paper as [91]
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Cites
What is Twitter, a social network or a news media?
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Measuring user influence in twitter: The million follower fallacy
Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, K. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14033 · Cited by 3,022 · cited 1×
“Another important social media study, based on Twitter, speaks of in-degree (number of followers) as a user’s popularity, and retweets as influence (Cha et al., 2010).”
BackgroundSocial Media and the Decision to Participate in Political Protest: Observations From Tahrir Square
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Whose space? differences among users and non-users of social network sites
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Metadata
- Authors
- Tufekci, Z.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2014-05-16
- Volume
- 8
- Issue
- 1
- Pages
- 505-514
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W2102861780
- Cited by
- 780
- References
- 27
- Citations by year
- 2026: 52 · 2025: 49 · 2024: 54 · 2023: 60 · 2022: 58 · 2021: 98 · 2020: 73 · 2019: 71 · 2018: 91 · 2017: 72 · 2016: 59 · 2015: 34 · 2014: 9
- FWCI
- 117.22
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Keywords
- social media big data, representativeness, validity, sampling bias, Twitter, hashtags, sampling frame, field effect, network analysis, methodological pitfalls
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.
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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