Fake news, disinformation and misinformation in social media: a review

In the paper

“Similarly, other researchers concurred that even the best AI for spotting fake news is still ineffective.18”

What the sources say

• ?Could not verify

  The Implications section describes technical approaches to detecting misinformation, including supervised models and natural-language processing, but does not assess whether even the best AI is ineffective. Searches for AI detection effectiveness and limitations found no passage making that claim; the paper instead focuses on social engagement metrics. (LLM, conf=0.72)

  cites [18] Avram M, Micallef N, Patil S, Menczer F (2020) Exposure to social engagement metrics increases vulnerability to misinformation. arXiv preprint arxiv:2005.04682, https://doi.org/10.37016/mr-2020-033 sourceExperimental — accuracy not yet measured

In the paper

“Some consider fake news as a type of misinformation (Allen et al. 2020; Singh et al. 2021; Ha et al. 2021; Pennycook and Rand 2019; Shao et al. 2018b; Di Domenico et al. 2021; Sharma et al. 2019; Celliers and Hattingh 2020; Klein and Wueller 2017; Potthast et al. 2017; Islam et al. 2020), others consider it as a type of disinformation”

What the sources say

• Supported

  The Introduction (page not specified) discusses fake news as deliberately engineered online misinformation, supporting the part of the citing sentence attached to Allen et al. The citation is not attached to the later claim about disinformation. (LLM, conf=0.88)

  cites Allen et al. 2020 source
• ?Not checked — no full text of the sourcecites Singh et al. 2021 source
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Ha et al. 2021

In the paper

“Moreover, fact-checking organizations cannot keep up with the dynamics of propagation as they require human verification, which can hold back a timely and cost-effective response (Kim et al. 2018; Ruchansky et al. 2017; Shu et al. 2018a).”

What the sources say

• Partly supported

  The Introduction (p. 1) says fact-checking is costly and that independent reviewers can check only a small percentage of stories spreading over time, supporting the claim about limited capacity and cost. It does not say that human verification causes delays or that organizations cannot keep pace with propagation; the model instead assumes instantaneous verification once a story is sent for checking (Problem formulation, p. 3). (LLM, conf=0.85)

  cites Kim et al. 2018 sourceExperimental — accuracy not yet measured
• Partly supported

  The Introduction (p. 1) says fact-checking websites require expert analysis, which inhibits a timely response. It does not establish that they cannot keep up with the speed of propagation or that their response is not cost-effective. (LLM, conf=0.86)

  cites Ruchansky et al. 2017 sourceExperimental — accuracy not yet measured
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Shu et al. 2018a

In the paper

“Furthermore, most of these studies ignore at least one of the mentioned perspectives, and in many cases, they do not cover other existing detection approaches using methods such as blockchain and fact-checking, as well as analysis on metrics used for Search Engine Optimization (Mazzeo and Rapisarda 2022).”

What the sources say

• Partly supported

  The Introduction (page unavailable) says SEO metrics have not been widely used to identify fake-news sources, supporting the SEO portion of the claim. The cited passages do not establish that most existing reviews omit a perspective or fail to cover blockchain and fact-checking approaches; the paper itself describes using fact-checking sources for data labeling. (LLM, conf=0.77)

  cites Mazzeo and Rapisarda 2022 sourceExperimental — accuracy not yet measured

In the paper

“Hence, fake news can have a significant impact on society as manipulated and false content is easier to generate and harder to detect (Kumar and Shah 2018)”

What the sources say

• Partly supported

  The introduction (p. 2) and Section 4 (p. 10) describe substantial social impacts of false information. Section 6 (p. 21) says false information is made to appear true and is therefore harder to identify. The discussion on p. 8 says it can be generated automatically, but does not establish the claim that manipulated false content is easier to create or that this ease of creation explains its societal impact. (LLM, conf=0.83)

  cites Kumar and Shah 2018 sourceExperimental — accuracy not yet measured

In the paper

“The first definition of the term fake news was provided by Allcott and Gentzkow (2017) as news articles that are intentionally and verifiably false and could mislead readers.”

What the sources say

• Partly supported

  The “Definition and History” section gives the quoted definition almost verbatim. It does not say that Allcott and Gentzkow provided the first definition of the term, so the priority claim is unsupported. Page numbers were not available in our copy. (LLM, conf=0.88)

  cites Allcott and Gentzkow (2017) sourceExperimental — accuracy not yet measured

In the paper

“For instance, people with little knowledge about the issue compared to those who are strongly concerned about the key issue of fake news tend to be easier to convince that the misleading or fake news is real, especially when shared via a video modality as compared to the text or the audio modality (Demuyakor and Opata 2022).”

What the sources say

• Supported

  The Conclusions state that people with little knowledge of an issue are easier to convince that misleading or fake news is real than people strongly concerned with the issue, especially when it is presented as video rather than text or audio. This directly matches the attributed claim. (LLM, conf=0.95)

  cites Demuyakor and Opata 2022 source

In the paper

“They show that leveraging personality traits and linguistic patterns can improve the performance in differentiating between checkers and spreaders.”

What the sources say

• Supported

  The paper’s Abstract explicitly states that experimental results show linguistic patterns and personality traits can improve performance in differentiating checkers from spreaders. The Conclusions likewise report that both were useful for the task. (LLM, conf=0.95)

  cites Giachanou et al. 2020 source

In the paper

“However, they are not necessarily false and may turn out to be true (Zubiaga et al. 2018).”

What the sources say

• Supported

  Section 11.1, “Open Challenges and Future Research Directions,” states that rumours are not necessarily false and may turn out to be true. Section 1.1 likewise says that unverified information may prove true, false, or remain unresolved. (LLM, conf=0.95)

  cites Zubiaga et al. 2018 source

In the paper

“Conspiracy belief refers to people’s adoption and belief of conspiracy theories, and it is associated with psychological, political and social factors (Douglas et al. 2019).”

What the sources say

• Supported

  The Psychological Factors and Social Motives sections describe psychological and social motives associated with conspiracy belief. The Political Factors section discusses political circumstances and states that political factors can drive conspiracy belief. Together, these passages support the citing sentence’s broad characterization. (LLM, conf=0.92)

  cites Douglas et al. 2019 source

In the paper

“For instance, lately and during the COVID-19 pandemic, conspiracy theories have been discussed from a public health perspective (Meese et al. 2020; Allington et al. 2020; Freeman et al. 2020).”

What the sources say

• Supported

  In Section II, ‘Conspiracy at the ground level: protests and arson’ (page unavailable), the paper describes the Australian Chief Medical Officer and state health departments publicly addressing COVID-19–5G misinformation. That supports the broad claim that pandemic conspiracy theories were discussed from a public-health perspective. (LLM, conf=0.88)

  cites Meese et al. 2020 source
• Supported

  The Introduction frames COVID-19 conspiracy beliefs as a potential public-health risk. The Discussion examines their association with COVID-19-specific health-protective behaviours. Page numbers were not supplied in our copy. (LLM, conf=0.94)

  cites Allington et al. 2020 source
• Supported

  The Introduction examines whether COVID-19 conspiracy beliefs affect adherence to measures intended to protect the population. The Discussion explicitly considers their potential effects on social distancing and disease spread, supporting the description of a public-health perspective. Page numbers are not provided in our copy. (LLM, conf=0.94)

  cites Freeman et al. 2020 source

In the paper

“found that individuals most likely to engage with fake news sources were generally conservative-leaning, older, and highly engaged with political news.”

What the sources say

• ?Could not verify

  The Partisanship of Fake News section reports that pro-Trump stories were shared more often, but does not identify the people who engaged with fake-news sources. The Who Believes Fake News section associates older age and greater media consumption with more accurate judgments about headlines, not greater engagement with fake-news sources. Our copy does not establish the claimed demographic profile. (LLM, conf=0.76)

  cites Allcott and Gentzkow 2017 sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites Grinberg et al. 2019 source
• Partly supported

  The Results and Discussion (pages not provided) support the findings that older and more conservative respondents were more likely to share links from fake-news domains on Facebook during the 2016 election. They do not establish that these respondents were highly engaged with political news: the study measured sharing rather than engagement with news sources, and the Results say that people who shared the most content overall were, if anything, less likely to share from fake-news domains. The Discussion describes the finding as occurring during the election campaign, not after it. (LLM, conf=0.89)

  cites Guess et al. 2019 sourceExperimental — accuracy not yet measured

In the paper

“forms of fake news may include false text such as hyperlinks or embedded content; multimedia such as false videos (Demuyakor and Opata 2022), images (Masciari et al. 2020; Shen et al. 2019), audios (Demuyakor and Opata 2022) and so on. Moreover, we can also find multimodal content (Shu et al. 2020a)”

What the sources say

• Supported

  The Introduction describes fake news on Facebook in doctored video and audio formats. Demuyakor and Opata are cited for the video and audio examples in the citing sentence, not for hyperlinks, images, or multimodal content. (LLM, conf=0.92)

  cites Demuyakor and Opata 2022 source
• ?Not checked — no full text of the sourcecites Masciari et al. 2020 source
• Supported

  The Shen et al. citation is attached specifically to “images,” not to the sentence’s other forms of fake news. The paper’s Images and mockups section (p. 11) describes six fake images used in its study, supporting that example. (LLM, conf=0.94)

  cites Shen et al. 2019 source

In the paper

“Social context-based aspects can be classified into two subcategories, user-based and network-based, and they can be used for context analysis and training tasks in the case of AI- and ML-based approaches.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Shu et al. 2019b
• ?Could not verify

  Section 3.2 distinguishes individual-level psychological approaches from network-science approaches, but it does not classify social-context features as user-based and network-based. Sections 3.3 and 4.1 discuss content and network analyses, not using those two feature categories for context analysis or AI/ML training. Searches for the feature taxonomy and machine-learning training found no passage addressing the claim; page numbers were unavailable in our copy. (LLM, conf=0.73)

  cites Wang et al. 2019c sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites Hamdi et al. 2020 source

In the paper

“Next, they try to increase public exposure to the created content and thus boost its perceived credibility by targeting influential users that are more likely to believe disinformation in the hope of getting them to “repost” the fabricated content.”

What the sources say

• Partly supported

  Section 3.3, “Bots on Social Media,” says bots target people more likely to believe disinformation. It also says bots target influential users in hopes of a repost that increases exposure and perceived credibility. It does not establish that the influential users are the people more likely to believe disinformation. (LLM, conf=0.83)

  cites Shu et al. 2020a sourceExperimental — accuracy not yet measured

In the paper

“fake news is diverse and dynamic in terms of content, topics, publishing methods and media platforms, and sophisticated linguistic styles geared to emulate true news.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Shu et al. 2020d
• Partly supported

  The Introduction (p. 1) describes fake news about several topics, its spread on social media, and the dynamic nature of news articles. The Insight Analysis (p. 8) also finds that news-content distributions change over time. Our copy does not substantiate diverse publishing methods or sophisticated linguistic styles designed to emulate true news. (LLM, conf=0.86)

  cites Wang et al. 2020 sourceExperimental — accuracy not yet measured
• Partly supported

  Section 4.2 (page not provided) observes that fake-news writers use mainstream writing conventions, supporting the claim that their style can emulate genuine news. Section 3 mentions different dissemination techniques, but the paper focuses on 2016 US election articles; it does not establish the broader claim that fake news is diverse and dynamic across topics, publishing methods, and media platforms. (LLM, conf=0.84)

  cites Pathak and Srihari 2019 sourceExperimental — accuracy not yet measured

In the paper

“Ismailov et al. (2020) assert that the techniques used to detect bots depend on the social platform and the objective.”

What the sources say

• Supported

  Section 3.2 (Detection Features) states that the techniques used by identity-deception detection models depend on the social platform and the objective, explicitly giving bot detection as an example. This supports the citing sentence's paraphrase. (LLM, conf=0.95)

  cites Ismailov et al. 2020 source

In the paper

“Some even prove that deep learning techniques outperform traditional machine learning techniques (Mishra et al. 2022).”

What the sources say

• Supported

  The Literature Review reports that, in the paper’s fake-news detection comparisons, deep learning models exceeded conventional machine-learning models. This supports the citing sentence as a statement about some studies, rather than a claim that deep learning always performs better. (LLM, conf=0.90)

  cites Mishra et al. 2022 source

In the paper

“User behavior models primarily focus on features related to an individual user’s behavior, such as frequency of activities (e.g., number of tweets or posts per time interval), patterns of activity and clickstream sequences.”

What the sources say

• Supported

  Section 3.2.2, “User Behavior Models,” states the claim almost verbatim, including activity frequency, activity patterns, and clickstream sequences. (LLM, conf=0.95)

  cites Ismailov et al. 2020 source

In the paper

“Previous work by Yaqub et al. (2020) has shown that people lack trust in automated solutions for fake news detection”

What the sources say

• Partly supported

  The Study Design and Flow section says the study tested a warning attributed to AI algorithms, and Effect of Credibility Indicators reports that this warning was less effective than a fact-checker warning at reducing intent to share non-true headlines. That supports a narrower finding about responses to AI-labelled warnings, but the paper does not establish that users generally distrust automated fake-news detection tools or directly measure such trust. (LLM, conf=0.82)

  cites Yaqub et al. (2020) sourceExperimental — accuracy not yet measured

In the paper

“A prominent troll farm example is the Russia-based Internet Research Agency (IRA), which disseminated inflammatory content online to influence the outcome of the 2016 U.S. presidential election.33”

What the sources say

• ?Could not verify

  The introduction (p. 1) discusses fake news during the 2016 U.S. presidential election, but does not mention the Internet Research Agency or Russian efforts to influence the election. Searches for the agency, troll farms, and inflammatory online content found no passage addressing the attributed claim. (LLM, conf=0.76)

  cites [33] Brashier NM, Pennycook G, Berinsky AJ, Rand DG (2021) Timing matters when correcting fake news. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.2020043118 sourceExperimental — accuracy not yet measured

In the paper

“Similarly, Qian et al. (2018) assert that fake news and fabricated content propagate exponentially at the early stage of its creation and can cause a significant loss in a short amount of time (Friggeri et al. 2014) including manipulating the outcome of political events (Liu and Wu 2018; Bessi and Ferrara 2016).”

What the sources say

• Supported

  The Introduction states that fake news propagates exponentially at an early stage and can cause significant loss in a short time, giving a stock-value loss as an example. The political-events clause has separate citations and is not attributed to Qian et al. in the citing sentence. (LLM, conf=0.93)

  cites Qian et al. 2018 source
• Partly supported

  The Introduction (page unavailable) says social networks can spread both true and false information quickly, and the Conclusion says dubious rumors can propagate rapidly. The paper does not establish exponential growth at the early stage or significant losses caused within a short time. The political-outcomes clause is attached to other citations, not Friggeri et al. (LLM, conf=0.84)

  cites Friggeri et al. 2014 sourceExperimental — accuracy not yet measured
• Supported

  The Liu and Wu citation is attached to the claim that fake news can manipulate the outcome of political events. The Introduction (p. 1) states this directly. The sentence attributes exponential early propagation and short-term loss to other citations, so those points are not part of this verdict. (LLM, conf=0.94)

  cites Liu and Wu 2018 source

In the paper

“They note that a malicious bot designed to make friends with as many accounts as possible will require a different detection approach than a bot designed to repeatedly post links to malicious websites.”

What the sources say

• Supported

  Section 3.2, “Detection Features,” states this comparison directly: a bot designed to make many friends requires a different detection approach from one repeatedly posting links to malicious websites. (LLM, conf=0.95)

  cites Ismailov et al. 2020 source

In the paper

“First, they amplify interactions with content as soon as it is created to make it look legitimate and to facilitate its spread across social networks.”

What the sources say

• Partly supported

  Section 3.3, “Bots on Social Media,” says bots amplify interactions as soon as content is created to help it go viral. It attributes increased perceived credibility to a different strategy: getting influential users to repost the content. The source therefore supports the timing and spread, but not the claim that the initial amplification itself makes the content look legitimate. (LLM, conf=0.85)

  cites Shu et al. 2020a sourceExperimental — accuracy not yet measured

In the paper

“Feature extraction is considered as one of the best techniques to reduce data size in automatic fake news detection.”

What the sources say

• Partly supported

  The introductory discussion says reducing dataset dimensions can lower computational complexity and improve fake-news classification. Section III describes feature selection—removing redundant features—not feature extraction. The copy also does not rank it as one of the best techniques. (LLM, conf=0.84)

  cites Yazdi et al. 2020 sourceExperimental — accuracy not yet measured

In the paper

“Other researchers categorize false information on the web and social media based on its intent and knowledge (i.e., when there is a single ground truth).”

In the paper

“The fake news problem has been addressed by researchers from various perspectives related to different topics.”

In the paper

“The terms most used in the literature to refer, categorize and classify fake news can be summarized and defined as shown in Table 3, in which we capture the similarities and show the differences between the different terms based on two common key features, which are the intent and the authenticity of the news content.”

In the paper

“Then, other definitions were provided in the literature, but they all agree on the authenticity of fake news to be false (i.e., being non-factual). However, they disagree on the inclusion and exclusion of some related concepts such as satire, rumors, conspiracy theories, misinformation and hoaxes from the given definition.”

In the paper

“Hence, there is still no agreed definition of the term “fake news.””

In the paper

“Various categorizations of fake news have been provided in the literature.”

In the paper

“Moreover, we can find many terms and concepts in the literature that refer to fake news (Van der Linden et al. 2020; Molina et al. 2021)”

What the sources say

• ?Not checked — no full text of the sourcecites Van der Linden et al. 2020 source
• ?Not checked — no full text of the sourcecites Molina et al. 2021 source

In the paper

“The literature agrees on the definitions of the latter group, but there is still no agreed-upon definition of the first group.”

In the paper

“In Fig. 3, we identify the different features used in the literature to define fake news (i.e., intent, authenticity and knowledge).”

In the paper

“Hence, some definitions are based on two key features, which are authenticity and intent (i.e., news articles that are intentionally and verifiably false and could mislead readers). However, other definitions are based on either authenticity or intent.”

In the paper

“The existing terms can be separated into two groups. The first group represents the general terms, which are information disorder, false information and fake news, each of which includes a subset of terms from the second group. The second group represents the elementary terms, which are misinformation, disinformation and malinformation.”

In the paper

“Some of these terms are explicitly used to refer to fake news (i.e., disinformation, misinformation and false information), while others are not (i.e., malinformation).”

In the paper

“We can distinguish two major categories of fake news based on the studied perspective (i.e., intention or content) as shown in Fig. 4.”

In the paper

“However, the proposed blockchain approaches are few in number and they are fundamental and theoretical approaches.”

In the paper

“Indeed, fact-checking features are being adopted by multiple online social network platforms. For instance, Facebook34 started addressing false information through independent fact-checkers in 2017, followed by Google35 the same year. Two years later, Instagram36 followed suit.”

In the paper

“Most researchers consider fake news detection as a classification problem and use artificial intelligence techniques, as shown in Fig. 8.”

In the paper

“Most of them combine many AI techniques in their solutions rather than relying on one specific approach.”

In the paper

“Most of the news content-based approaches consider fake news detection as a classification problem and they use AI techniques such as classical machine learning (e.g., regression, Bayesian) as well as deep learning (i.e., neural methods such as CNN and RNN).”

In the paper

“Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial.”

In the paper

“falsehood spreads significantly farther, faster, deeper and more broadly than the truth in all categories of information”

What the sources say

• ?Not checked — no full text of the sourcecites Vosoughi et al. 2018 source

In the paper

“fake news, misinformation and disinformation are indeed parts of the larger landscape of online deception (Hage et al. 2021).”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Hage et al. 2021

In the paper

“Hence, malicious attackers are using more complex tools and strategies to deceive users.”

In the paper

“In the context of online deception, several factors need to be considered: the deceiver, the purpose or aim of the deception, the social media service, the deception technique and the potential target (Aïmeur et al. 2018; Hage et al. 2021).”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Aïmeur et al. 2018
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Hage et al. 2021

In the paper

“AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content.”

In the paper

“the spread of false news online is six times faster than truthful content”

What the sources say

• ?Not checked — no full text of the sourcecites Vosoughi et al. 2018 source

In the paper

“Deception relies on peoples’ trust, doubt and strong emotions that may prevent them from thinking and acting clearly (Aïmeur et al. 2018).”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Aïmeur et al. 2018

In the paper

“Fake news identification is still a complex unresolved issue.”

In the paper

“the content is designed in a way to closely resemble the truth”

In the paper

“70% of the users could not distinguish real from fake news (Vosoughi et al. 2018)”

What the sources say

• ?Not checked — no full text of the sourcecites Vosoughi et al. 2018 source

In the paper

“fake news detection on social media is still in the early age of development, and many challenging issues remain that require deeper investigation.”

In the paper

“In other words, deception gets people to do things they would not otherwise do.”

In the paper

“Furthermore, Vosoughi et al. (Vosoughi et al. 2018) found that contrary to conventional wisdom, robots have accelerated the spread of real and fake news at the same rate, implying that fake news spreads more than the truth because humans, not robots, are more likely to spread it.”

What the sources say

• ?Not checked — no full text of the sourcecites Vosoughi et al. 2018 source

In the paper

“However, it is also possible that people choose misinformation because they engage in directionally motivated reasoning (Badawy et al. 2019; Flynn et al. 2017).”

What the sources say

• ?Not checked — no full text of the sourcecites Badawy et al. 2019 source
• ?Not checked — no full text of the sourcecites Flynn et al. 2017 source

In the paper

“However, the goal behind using clickbait is to increase the traffic to a website (Zannettou et al. 2019).”

What the sources say

• ?Not checked — no full text of the sourcecites Zannettou et al. 2019 source

In the paper

“It is a consequential type of false information as it can change the course of human history (e.g., by changing the outcome of an election) (Zannettou et al. 2019).”

What the sources say

• ?Not checked — no full text of the sourcecites Zannettou et al. 2019 source

In the paper

“Propaganda types of fake news can be debunked with manual fact-based detection models such as the use of expert-based fact-checkers (Collins et al. 2020).”

What the sources say

• ?Not checked — no full text of the sourcecites Collins et al. 2020 source

In the paper

“Moreover, it is important to highlight that the existence of the overlap in the types of fake news mentioned above has been proven, thus it is possible to observe false information that may fall within multiple categories (Zannettou et al. 2019).”

What the sources say

• ?Not checked — no full text of the sourcecites Zannettou et al. 2019 source

In the paper

“However, most audiences believed the information passed in this satirical form because the comedian usually projects news from mainstream media and frames them to suit their program (Collins et al. 2020).”

What the sources say

• ?Not checked — no full text of the sourcecites Collins et al. 2020 source

In the paper

“Online clients are normally vulnerable and will, in general, perceive web-based networking media as reliable, as reported by Abdullah-All-Tanvir et al. (2019), who propose to mechanize fake news recognition.”

What the sources say

• ?Not checked — no full text of the sourcecites Abdullah-All-Tanvir et al. 2019 source

In the paper

“This is a complex issue because many people believe almost everything on the Internet and the ones who are new to digital technology or have less expertise may be easily fooled (Edgerly et al. 2020).”

What the sources say

• ?Not checked — no full text of the sourcecites Edgerly et al. 2020 source

In the paper

“Moreover, it has been widely proven (Metzger et al. 2020; Edgerly et al. 2020) that people are often motivated to support and accept information that goes with their preexisting viewpoints and beliefs, and reject information that does not fit in as well.”

What the sources say

• ?Not checked — no full text of the sourcecites Metzger et al. 2020 source
• ?Not checked — no full text of the sourcecites Edgerly et al. 2020 source

In the paper

“Results showed that headlines associated with a high number of likes increased credibility, thereby enhancing detection accuracy for real news but undermining accuracy for fake news.”

What the sources say

• ?Not checked — no full text of the sourcecites Luo et al. 2022 source

In the paper

“Recently, another classification was proposed by Zhang and Ghorbani (2020). It is based on the combination of content and intent to categorize fake news. They distinguish physical news content and non-physical news content from fake news.”

What the sources say

• ?Not checked — no full text of the sourcecites Zhang and Ghorbani (2020) source

In the paper

“Falling for disinformation may also be explained by a lack of critical thinking and of the need for evidence that supports information (Vilmer et al. 2018; Badawy et al. 2019).”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Vilmer et al. 2018
• ?Not checked — no full text of the sourcecites Badawy et al. 2019 source

In the paper

“Abdullah-All-Tanvir et al. (2020) reported that fake news tends to have more complicated stories and hardly ever make any references.”

What the sources say

• ?Not checked — no full text of the sourcecites Abdullah-All-Tanvir et al. 2020 source

In the paper

“Moreover, misinformed people are a greater problem than uninformed people (Kuklinski et al. 2000), because the former hold inaccurate opinions (which may concern politics, climate change, medicine) that are harder to correct.”

What the sources say

• ?Not checked — no full text of the sourcecites Kuklinski et al. 2000 source

In the paper

“They concluded that participants exhibit higher intent to verify the news only when they believe the headline to be true, which is predicted by perceived congruence with preexisting ideological tendencies.”

What the sources say

• ?Not checked — no full text of the sourcecites Edgerly et al. 2020 source

In the paper

“Astroturfing can affect different domains of interest, based on which online astroturfing can be mainly divided into political astroturfing, corporate astroturfing and astroturfing in e-commerce or online services (Mahbub et al. 2019).”

What the sources say

• ?Not checked — no full text of the sourcecites Mahbub et al. 2019 source

In the paper

“The effects of these forms of fake news content vary on the credibility assessment, as well as sharing intentions which influences the spread of fake news on OSNs.”

In the paper

“Conspiracy theories are widespread in contemporary democracies (Sutton and Douglas 2020), and they have major consequences.”

What the sources say

• ?Not checked — no full text of the sourcecites Sutton and Douglas 2020 source

In the paper

“Moreover, fake news appears in multiple types and forms, which makes it hard and challenging to define a single global solution able to capture and deal with the disseminated content.”

In the paper

“Consequently, they deduce that humans are naturally not very good at differentiating real information from fake information.”

In the paper

“we can mention as examples deepfake videos (Yang et al. 2019b) and GAN-generated fake images (Zhang et al. 2019b), which are artificial intelligence-based machine-generated fake content that are hard for unsophisticated social network users to identify.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Yang et al. 2019b
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Zhang et al. 2019b

In the paper

“It is worth noting that in addition to bots causing the outpouring of the majority of the misrepresentations, specific individuals are also contributing a large share of this issue (Abdullah-All-Tanvir et al. 2019).”

What the sources say

• ?Not checked — no full text of the sourcecites Abdullah-All-Tanvir et al. 2019 source

In the paper

“Social bots tend to connect with legitimate users instead of other bots. They try to act like a human with fewer words and fewer followers on social media. This contributes to the forwarding of fake news (Jiang et al. 2019).”

What the sources say

• ?Not checked — no full text of the sourcecites Jiang et al. 2019 source

In the paper

“They found that social bots played a disproportionate role in spreading articles from low-credibility sources by amplifying such content in the early spreading moments and targeting users with many followers through replies and mentions to expose them to this content and induce them to share it.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Shao et al. 2018a

In the paper

“fake news detection solutions can be categorized as news content-based approaches, the social context-based approaches that can be divided into network and user-based approaches, and hybrid approaches.”

In the paper

“Review bombing refers to coordinated groups of people massively performing the same negative actions online (e.g., dislike, negative review/comment) on an online video, game, post, product, etc., in order to reduce its aggregate review score.”

What the sources say

• ?Not checked — no full text of the sourcecites Moro and Birt 2022 source

In the paper

“Moreover, there is a difference between bot-generated and human-written clickbait (Le et al. 2019).”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Le et al. 2019

In the paper

“Therefore, it is crucial to note that the dynamic nature of the various online social platforms, which results in the continued rapid and exponential propagation of such fake content, remains a major challenge that requires further investigation while defining innovative solutions for fake news detection.”

In the paper

“As a result, Twitter suspended accounts connected to the IRA and deleted 200,000 tweets from Russian trolls (Jamieson 2020).”

What the sources say

• ?Not checked — no full text of the sourcecites Jamieson 2020 source

In the paper

“However, fake news identification cannot be addressed solely through human force since it demands a lot of effort in terms of time and cost, and it is ineffective in terms of preventing the fast spread of fake content.”

In the paper

“Lastly, Abdullah-All-Tanvir et al. (2020) note that it is almost impossible to manually detect the sources and authenticity of fake news effectively and efficiently, due to its fast circulation in such a small amount of time.”

What the sources say

• ?Not checked — no full text of the sourcecites Abdullah-All-Tanvir et al. 2020 source

In the paper

“A recent classification by Sharma et al. (2019) uses three categories of fake news identification methods. Each category is further divided based on the type of existing methods (i.e., content-based, feedback-based and intervention-based methods).”

What the sources say

• ?Not checked — no full text of the sourcecites Sharma et al. 2019 source

In the paper

“They demonstrated the potential of using these features to detect fake news and they showed their effectiveness for fake news detection as well.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Shu et al. 2020b

In the paper

“Unlike traditional machine learning approaches, which are based on manually crafted features, deep learning approaches can learn hidden representations from simpler inputs both in context and content variations (Bondielli and Marcelloni 2019).”

What the sources say

• ?Not checked — no full text of the sourcecites Bondielli and Marcelloni 2019 source

In the paper

“found that such content is produced and disseminated faster and easier through social media because of the low barriers that prevent doing so.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites Shu et al. 2020c

In the paper

“The fact-checking task is commonly manually performed by journalists to verify the truthfulness of a given claim.”

In the paper

“Few of them have focused on the automatic detection of fake news using artificial intelligence techniques.”

In the paper

“The review bombers can be both humans and bots coordinated in order to cause harm and mislead people by falsifying facts.”

In the paper

“It is not social bots, but it is a group of organized individuals engaging in trolling or bot-like promotion of narratives in a coordinated fashion (Wardle 2018) hired to massively spread fake news or any other harmful content.”

What the sources say

• ?Not checked — no full text of the sourcecites Wardle 2018 source

In the paper

“Moreover, capturing users’ historical behavior, including their emotions and/or opinions toward news content, can help in the early detection and mitigation of fake news.”

In the paper

“The context-based approaches explore the surrounding data outside of the news content, which can be an effective direction and has some advantages in areas where the content approaches based on text classification can run into issues.”

In the paper

“However, contextual information derived from social media users and from the network can provide useful auxiliary information to increase detection accuracy.”

In the paper

“Our choice of sources of information and search criteria may have introduced biases in our research. If so, it would be desirable to identify those biases and mitigate them.”