Dataset Information

The spread of low-credibility content by social bots.

ABSTRACT: The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.

SUBMITTER: Shao C

PROVIDER: S-EPMC6246561 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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The spread of low-credibility content by social bots.

Shao Chengcheng C Ciampaglia Giovanni Luca GL Varol Onur O Yang Kai-Cheng KC Flammini Alessandro A Menczer Filippo F

Nature communications 20181120 1

The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find ...[more]

PMID: 30459415

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Project description:BACKGROUND:Nutrition science is currently facing issues regarding the public's perception of its credibility, with social media (SM) influencers increasingly becoming a key source for nutrition-related information with high engagement rates. Source credibility and, to an extent, authenticity have been widely studied in marketing and communications but have not yet been considered in the context of nutrition or health communication. Thus, an investigation into the factors that impact perceived source and message credibility and authenticity is of interest to inform health communication on SM. OBJECTIVE:This study aims to explore the factors that impact message and source credibility (which includes trustworthiness and expertise) or authenticity judgments on SM platforms to better inform nutrition science SM communication best practices. METHODS:A total of 6 databases across a variety of disciplines were searched in March 2019. The inclusion criteria were experimental studies, studies focusing on microblogs, studies focusing on healthy adult populations, and studies focusing on either source credibility or authenticity. Exclusion criteria were studies involving participants aged under 18 years and clinical populations, gray literature, blogs, WeChat conversations, web-based reviews, non-English papers, and studies not involving participants' perceptions. RESULTS:Overall, 22 eligible papers were included, giving a total of 25 research studies. Among these studies, Facebook and Twitter were the most common SM platforms investigated. The most effective communication style differed depending on the SM platform. Factors reported to impact credibility included language used online, expertise heuristics, and bandwagon heuristics. No papers were found that assessed authenticity. CONCLUSIONS:Credibility and authenticity are important concepts studied extensively in the marketing and communications disciplines; however, further research is required in a health context. Instagram is a less-researched platform in comparison with Facebook and Twitter.

Project description:As e-cigarette use rapidly increases in popularity, data from online social systems (Twitter, Instagram, Google Web Search) can be used to capture and describe the social and environmental context in which individuals use, perceive, and are marketed this tobacco product. Social media data may serve as a massive focus group where people organically discuss e-cigarettes unprimed by a researcher, without instrument bias, captured in near real time and at low costs.This study documents e-cigarette-related discussions on Twitter, describing themes of conversations and locations where Twitter users often discuss e-cigarettes, to identify priority areas for e-cigarette education campaigns. Additionally, this study demonstrates the importance of distinguishing between social bots and human users when attempting to understand public health-related behaviors and attitudes.E-cigarette-related posts on Twitter (N=6,185,153) were collected from December 24, 2016, to April 21, 2017. Techniques drawn from network science were used to determine discussions of e-cigarettes by describing which hashtags co-occur (concept clusters) in a Twitter network. Posts and metadata were used to describe where geographically e-cigarette-related discussions in the United States occurred. Machine learning models were used to distinguish between Twitter posts reflecting attitudes and behaviors of genuine human users from those of social bots. Odds ratios were computed from 2x2 contingency tables to detect if hashtags varied by source (social bot vs human user) using the Fisher exact test to determine statistical significance.Clusters found in the corpus of hashtags from human users included behaviors (eg, #vaping), vaping identity (eg, #vapelife), and vaping community (eg, #vapenation). Additional clusters included products (eg, #eliquids), dual tobacco use (eg, #hookah), and polysubstance use (eg, #marijuana). Clusters found in the corpus of hashtags from social bots included health (eg, #health), smoking cessation (eg, #quitsmoking), and new products (eg, #ismog). Social bots were significantly more likely to post hashtags that referenced smoking cessation and new products compared to human users. The volume of tweets was highest in the Mid-Atlantic (eg, Pennsylvania, New Jersey, Maryland, and New York), followed by the West Coast and Southwest (eg, California, Arizona and Nevada).Social media data may be used to complement and extend the surveillance of health behaviors including tobacco product use. Public health researchers could harness these data and methods to identify new products or devices. Furthermore, findings from this study demonstrate the importance of distinguishing between Twitter posts from social bots and humans when attempting to understand attitudes and behaviors. Social bots may be used to perpetuate the idea that e-cigarettes are helpful in cessation and to promote new products as they enter the marketplace.

Dataset Information

The spread of low-credibility content by social bots.

Publications

The spread of low-credibility content by social bots.

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