Identifying Fake News from the Variables that Governs the Spread of Fake News

Dordevic, Milan; Pourghomi, Pardis; Safieddine, Fadi

doi:10.1109/smap49528.2020.9248453

Cited by 5 publications

(1 citation statement)

References 17 publications

(18 reference statements)

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“…While platforms have expressed their commitment, begun to implement policies, and heavily invested in mitigating the spread of disinformation, disinformation continues to be a pressing challenge and these policies continue to evolve. Djordjevic (2020) reviews the strategies social media companies have implemented in combating fake news. As of May 2022, Twitter announced a crisis misinformation policy which would place a warning over (and not amplify or recommend) severely harmful crisis-related content that is deemed as misleading (as verified by "multiple credible, publicly available sources") (Roth, 2022); whether such content will be identified by humans, algorithms, or a combination is not discussed.…”

Section: Social Media Algorithmic Landscapementioning

confidence: 99%

AI-driven disinformation: a framework for organizational preparation and response

Karinshak,

Jin

2023

JCOM

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PurposeDisinformation, false information designed with the intention to mislead, can significantly damage organizational operation and reputation, interfering with communication and relationship management in a wide breadth of risk and crisis contexts. Modern digital platforms and emerging technologies, including artificial intelligence (AI), introduce novel risks in crisis management (Guthrie and Rich, 2022). Disinformation literature in security and computer science has assessed how previously introduced technologies have affected disinformation, demanding a systematic and coordinated approach for sustainable counter-disinformation efforts. However, there is a lack of theory-driven, evidence-based research and practice in public relations that advises how organizations can effectively and proactively manage risks and crises driven by AI (Guthrie and Rich, 2022).Design/methodology/approachAs a first step in closing this research-practice gap, the authors first synthesize theoretical and technical literature characterizing the effects of AI on disinformation. Upon this review, the authors propose a conceptual framework for disinformation response in the corporate sector that assesses (1) technologies affecting disinformation attacks and counterattacks and (2) how organizations can proactively prepare and equip communication teams to better protect businesses and stakeholders.FindingsThis research illustrates that future disinformation response efforts will not be able to rely solely on detection strategies, as AI-created content quality becomes more and more convincing (and ultimately, indistinguishable), and that future disinformation management efforts will need to rely on content influence rather than volume (due to emerging capabilities for automated production of disinformation). Built upon these fundamental, literature-driven characteristics, the framework provides organizations actor-level and content-level perspectives for influence and discusses their implications for disinformation management.Originality/valueThis research provides a theoretical basis and practitioner insights by anticipating how AI technologies will impact corporate disinformation attacks and outlining how companies can respond. The proposed framework provides a theory-driven, practical approach for effective, proactive disinformation management systems with the capacity and agility to detect risks and mitigate crises driven by evolving AI technologies. Together, this framework and the discussed strategies offer great value to forward-looking disinformation management efforts. Subsequent research can build upon this framework as AI technologies are deployed in disinformation campaigns, and practitioners can leverage this framework in the development of counter-disinformation efforts.

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Section: Social Media Algorithmic Landscapementioning

confidence: 99%

AI-driven disinformation: a framework for organizational preparation and response

Karinshak,

Jin

2023

JCOM

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Novel approaches to fake news and fake account detection in OSNs: user social engagement and visual content centric model

Uppada

Manasa

Vidhathri

et al. 2022

Soc. Netw. Anal. Min.

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With an increase in the number of active users on OSNs (Online Social Networks), the propagation of fake news became obvious. OSNs provide a platform for users to interact with others by expressing their opinions, resharing content into different networks, etc. In addition to these, interactions with posts are also collected, termed as social engagement patterns. By taking these social engagement patterns (by analyzing infectious disease spread analogy), SENAD(Social Engagement-based News Authenticity Detection) model is proposed, which detects the authenticity of news articles shared on Twitter based on the authenticity and bias of the users who are engaging with these articles. The proposed SENAD model incorporates the novel idea of authenticity score and factors in user social engagement centric measures such as Following-followers ratio, account age, bias, etc. The proposed model significantly improves fake news and fake account detection, as highlighted by classification accuracy of 93.7%. Images embedded with textual data catch more attention than textual messages and play a vital role in quickly propagating fake news. Images published have distinctive features which need special attention for detecting whether it is real or fake. Images get altered or misused to spread fake news. The framework Credibility Neural Network (CredNN) is proposed to assess the credibility of images on OSNs, by utilizing the spatial properties of CNNs to look for physical alterations in an image as well as analyze if the image reflects a negative sentiment since fake images often exhibit either one or both characteristics. The proposed hybrid idea of combining ELA and Sentiment analysis plays a prominent role in detecting fake images with an accuracy of around 76%.

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Accuracy measure for identification of fake blog articles in social website using random forest over support vector machine

Krishna,

Divya

2024

16th International Engineering and Computing Research Conference (Eureca)

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Identifying Fake News from the Variables that Governs the Spread of Fake News

Cited by 5 publications

References 17 publications

AI-driven disinformation: a framework for organizational preparation and response

AI-driven disinformation: a framework for organizational preparation and response

Novel approaches to fake news and fake account detection in OSNs: user social engagement and visual content centric model

Accuracy measure for identification of fake blog articles in social website using random forest over support vector machine

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