Truth and Lies: Deception and Cognition on the Internet
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ItemA Tale of Two Internet News Platforms-Real vs. Fake: An Elaboration Likelihood Model Perspective( 2018-01-03)This paper presents findings from a field analysis of real vs. fake news propagated on the Internet. Elaboration Likelihood Model (ELM) was used as a theoretical framework to investigate information presentation mechanisms used by real and fake content generators to persuade readers. ELM theorizes two routes through which information can inform attitudinal changes: a central route of high cognitive effort, and a peripheral route of low cognitive effort. We hypothesize that fake news sites favor the peripheral route by providing less information overall, and by providing more negative affective cues. Data was gathered from Internet platforms that publish real news and fake news. Results indicate that the amount of information disseminated by fake news platforms is lower than that of reputable platforms. Content analysis reveals that fake news with business impact are typically more negative in their valence compared to real news. Implications of our findings for theory and practice are discussed.
ItemCombating Fake News: An Investigation of Information Verification Behaviors on Social Networking Sites( 2018-01-03)The use of the term -˜fake news’ has recently become widespread; however, research on fake news is limited. This research intends to increase understanding of how users of social networking sites (SNSs) determine if they should confirm the validity of news content. Grounded in research on the epistemology of testimony, we develop and test a research model based on perceptions related to news authors, news sharers, and users to test verification behaviors of users. The findings indicate that social tie variety, perceived cognitive homogeneity, trust in network, fake news awareness, perceived media credibility and intention to share influence an individual’s news verification behavior. We discuss the implications of our findings for SNS designers as well as users. We also integrate the theoretical perspectives of trust and testimony and demonstrate their value for explicating verification behaviors.
ItemDisruption and Deception in Crowdsourcing: Towards a Crowdsourcing Risk Framework( 2018-01-03)While crowdsourcing has become increasingly popular among organizations, it also has become increasingly susceptible to unethical and malicious activities. This paper discusses recent examples of disruptive and deceptive efforts on crowdsourcing sites, which impacted the confidentiality, integrity, and availability of the crowdsourcing efforts’ service, stakeholders, and data. From these examples, we derive an organizing framework of risk types associated with disruption and deception in crowdsourcing based on commonalities among incidents. The framework includes prank activities, the intentional placement of false information, hacking attempts, DDoS attacks, botnet attacks, privacy violation attempts, and data breaches. Finally, we discuss example controls that can assist in identifying and mitigating disruption and deception risks in crowdsourcing.
ItemSays Who?: How News Presentation Format Influences Perceived Believability and the Engagement Level of Social Media Users( 2018-01-03)We investigate whether the news presentation format affects the believability of a news story and the engagement level of social media users. Specifically, we test to see if highlighting the source delivering the story can nudge the users to think more critically about the truthfulness of the story that they see, and for obscure sources, whether source ratings can affect how the users evaluate the truthfulness. We also test whether the believability can influence the users' engagement level for the presented news post (e.g., read, like, comment, and share). We find that such changes in the news presentation format indeed have significant impacts on how social media users perceive and act on news items.