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This research aims to explores consumers' motives and attitudes toward misinformation (fake reviews), its characteristics and its impact on individuals, brands and firms.

A thematic analysis was undertaken to meet the research objectives by analyzing qualitative data from in-depth interviews with a diverse sample (N = 48).

The findings indicate that altruism, impression management, matching ideologies, message appeal and perceived source power are the critical motivations for individuals to share misinformation. Misinformation leads to conflicts and avoidance among individuals and harms brand's reputation.

This study utilizes thematic analysis to extend and contribute to the literature on misinformation. The current research provides an overarching framework to decode the misinformation phenomenon for researchers and practitioners.

This study offers valuable insights to marketers to develop strategies to tackle the menace of false information to safeguard brand reputation.

At a time when the gradual collapse of democratic norms and processes is obvious to anyone who cares to read the headlines, the tension between self and society is fertile soil for understanding democratic decay. While we may wish to see democracy refortified, the fact remains that citizens equipped to handle democratic practices are a necessary precondition for democratic revitalization. Yet, the deterioration of democracy suggests breakdown in the gears of democratic production of the democratic citizen. The following chapter examines a particular cancer that is antithetical to democracy and has afflicted your author – the authoritarian personality. Critical theorists and social scientists in the mid-twentieth century identified this personality disposition as one that cultivates receptivity to fascism and is today the beating heart of right-wing extremism in its particular incarnation as Trumpism. I develop the theory of the authoritarian personality as it shaped and inflamed at the familial, societal, and global levels. Contributing to the project of planetary sociology, I demonstrate how the changes occurring on the world stage incite the most pernicious and antidemocratic features of the authoritarian personality. All the while, I subject myself to critical scrutiny in order to illustrate the inner-workings of this personality disposition. Your author stands before you as a recovering authoritarian and hopes that by reading this chapter, you will begin to see authoritarianism all around you, perhaps even within yourself.

To provide human judgment input for computer algorithm development, this study examines the relative importance of source, content, and style cues in predicting the truthfulness ratings of two common types of online health information: news stories and institutional news releases.

This study employed a multi-method approach using (1) a manual content analysis of 400 randomly selected online health news stories and news releases from HealthNewsReview.org and (2) an online experiment comparing truthfulness ratings between news stories and news releases.

Using content analysis, the authors found significant differences in the importance of source, content, and style cues in predicting truthfulness ratings of news stories and news releases: source and style cues predicted truthfulness ratings better than content cues. In the experiment, source credibility was the most important predictor of truthfulness ratings, controlling for individual differences. Experts have higher ratings for news media stories than news releases and lay people have no differences in rating the two news formats.

It is important for health educators to curb consumer trust in misinformation and increase health information literacy. Rather than solely reporting scientific evidence, educators should focus on addressing cues people use to judge the truthfulness of health information.

This is the first study that directly compares human judgments of health news stories and news releases. Using both the breadth of content analysis and experimental causality testing, the authors evaluate the relative importance of source, content, and style cues in predicting truthfulness ratings.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.