This study investigates how citizens select election news online. Voluntary national samples (n = 372) browsed a news website featuring four types of election news (horserace, candidates' issue positions, campaign trails, and voters). Their online activities, including article selection and the length of exposure, were unobtrusively measured by behavior tracking software. The results revealed that participants tended to choose issue-based election coverage but avoided news stories about campaign trails. The horserace was not more popular than the other types of election news. The findings also supported negative bias by showing that participants preferred election news headlines that contained negative words.

This study proposes the idea of justificatory information forefending, a cognitive process by which individuals accept information that confirms their preexisting health beliefs, and reject information that is dissonant with their attitudes. In light of the sheer volume of often contradictory information related to health that is frequently highlighted by the traditional media, this study sought to identify antecedents and outcomes of this justificatory information forefending. Results indicate that individuals who are exposed to contradictory health information, currently engage in risky health behavior, are comfortable using the Internet to search for information, and are currently taking steps to maintain their health are likely to actively select health information that confirms their preexisting notions about their health, and to reject information that is contradictory to their beliefs. Additionally, individuals who engage in justificatory information forefending were also found to continue to engage in risky health behavior. Implications for theory and practice are discussed.

Personal lies (girl on date lying to dad) and fake news ( Obama Bans Pledge of Allegiance) both deceive but in different ways, so they require different detection methods. People in long-term relationships try to tell undetectable lies to encourage, often, audience inaction. In contrast, unattached fake news welcome attention and try to ignite audience action. Thus, they differ in six ways: (a) speaker–audience relationship, (b) goal, (c) emotion, (d) information, (e) number of participants, and (f) citation of sources. To detect personal lies, a person can use their intimate relationship to heighten emotions, raise the stakes, and ask for more information, participants, or sources. In contrast, a person evaluates the legitimacy of potential fake news by examining the websites of its author, the people in the news article, and/or reputable media sources. Large social media companies have suitable expertise, data, and resources to reduce fake news. Search tools, rival news media links to one another’s articles, encrypted signature links, and improved school curricula might also help users detect fake news.

Humans are not very good at detecting deception. The problem is that there is currently no other particular way to distinguish fake opinions in a comments section than by resorting to poor human judgments. For years, most scholarly and industrial efforts have been directed at detecting fake consumer reviews of products or services. A technique for identifying deceptive opinions on social issues is largely underexplored and undeveloped. Inspired by the need for a reliable deceptive comment detection method, this study aims to develop an automated machine-learning technique capable of determining opinion trustworthiness in a comment section. In the process, we have created the first large-scale ground truth dataset consisting of 866 truthful and 869 deceptive comments on social issues. This is also one of the first attempts to detect comment deception in Asian languages (in Korean, specifically). The proposed machine-learning technique achieves nearly 81% accuracy in detecting untruthful opinions about social issues. This performance is quite consistent across issues and well beyond that of human judges.

No abstract