Characterizing the role of bots’ in polarized stance on social media

A global comparison of social media bot and human characteristics

Chatter on social media about global events comes from 20% bots and 80% humans. The chatter by bots and humans is consistently different: bots tend to use linguistic cues that can be easily automated (e.g., increased hashtags, and positive terms) while humans use cues that require dialogue understanding (e.g. replying to post threads). Bots use words in categories that match the identities they choose to present, while humans may send messages that are not obviously related to the identities they present. Bots and humans differ in their communication structure: sampled bots have a star interaction structure, while sampled humans have a hierarchical structure. These conclusions are based on a large-scale analysis of social media tweets across ~ 200 million users across 7 events. Social media bots took the world by storm when social-cybersecurity researchers realized that social media users not only consisted of humans, but also of artificial agents called bots. These bots wreck havoc online by spreading disinformation and manipulating narratives. However, most research on bots are based on special-purposed definitions, mostly predicated on the event studied. In this article, we first begin by asking, "What is a bot?", and we study the underlying principles of how bots are different from humans. We develop a first-principle definition of a social media bot. This definition refines existing academic and industry definitions: "A Social Media Bot is An automated account that carries out a series of mechanics on social media platforms, for content creation, distribution and collection, and/or for relationship formation and dissolutions." With this definition as a premise, we systematically compare the characteristics between bots and humans across global events, and reflect on how the software-programmed bot is an Artificial Intelligent algorithm, and its potential for evolution as technology advances. Based on our results, we provide recommendations for the use of bots and for the regulation of bots. Finally, we discuss three open challenges and future directions of the study of bots: Detect, to systematically identify these automated and potentially evolving bots; Differentiate, to evaluate the goodness of the bot in terms of their content postings and relationship interactions; Disrupt, to moderate the impact of malicious bots, while not unsettling human conversations.

An Analysis of Social Media Engagement and Conventional Bibliometrics for Articles Related to Distal Radius Fractures

PURPOSE

As methods of research publication and promotion evolve, conventional bibliometric analyses may not provide a complete representation of audience engagement with peer-reviewed literature. Our purpose was to assess for correlations between social media engagement (Altmetric attention score [AAS]) and conventional article metrics (citation density [CD] and journal impact factor).

METHODS

Distal radius fracture (DRF) articles with the highest number of citations were identified using Web of Science and imported into Altmetric. The Altmetric database quantifies an article's online engagement and social media footprint to determine the AAS. We evaluated four metrics pertaining to the top 100 DRF articles: AAS, social media mentions (SMM), journal impact factor, and CD. Spearman's ρ was calculated between four pairings of these metrics. Confidence intervals corresponding to each Spearman's ρ were obtained via bootstrapping over 10,000 replications.

RESULTS

Of the 1,000 most frequently cited DRF articles, 333 (33%) generated an AAS. The AAS of the top 100 articles according to AAS ranged from 6 to 317 with a mean of 16. Articles were predominantly original research (71%), followed by reviews (21%). A moderately positive correlation (ρ = 0.55) between AAS and SMM was found. Citation density versus SMM and CD versus AAS were both found to be weakly positively correlated with a ρ of 0.34 and 0.30, respectively.

CONCLUSIONS

For the 1,000 most frequently cited articles related to DRFs, 33% generated an AAS. Citation density demonstrated weak, positive correlations with both SMM and AAS. In addition, we found a moderately positive correlation between AAS and SMM.

CLINICAL RELEVANCE

These data suggest that online and social media engagement is weakly correlated with increased citations for peer-reviewed articles related to DRFs. Although AAS cannot determine article quality or scientific merit, online dissemination of peer-reviewed research may be an effective means of promoting academic publications and increasing citations.

Posts Supporting Anti-Environmental Policy in Brazil are Shared More on Social Media

Weakening environmental laws supported by disinformation are currently of concern in Brazil. An example of disinformation is the case of the "firefighter cattle". Supporters of this idea believe that by consuming organic mass, cattle decrease the risk of fire in natural ecosystems. This statement was cited by a member of the Bolsonaro government in response to the unprecedented 2020 fires in the Pantanal, as well as in support of a new law that enables extensive livestock in protected areas of this biome. By suggesting that grazing benefits the ecosystem, the "firefighter cattle" argument supports the interests of agribusiness. However, it ignores the real costs of livestock production on biodiversity. We analysed the social repercussion of the "firefighter cattle" by analysing public reactions to YouTube, Facebook, and Google News posts. These videos and articles and the responses to them either agreed or disagreed with the "firefighter cattle". Supportive posts were shared more on social media and triggered more interactions than critical posts. Even though many netizens disagreed with the idea of "firefighter cattle", it has gone viral, and was used as a tool to strengthen anti-environmental policies. We advocate that government institutions should use resources and guidelines provided by the scientific community to raise awareness. These materials include international reports produced by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) and the Intergovernmental Panel on Climate Change (IPCC). We need to curb pseudoscience and misinformation in political discourse, avoiding misconceptions that threaten natural resources and confuse global society.

Susceptibility to COVID-19 Nutrition Misinformation and Eating Behavior Change during Lockdowns: An International Web-Based Survey

To understand the susceptibility to nutrition-health misinformation related to preventing, treating, or mitigating the risk of COVID-19 during the initial lockdowns around the world, the present international web-based survey study (15 April-15 May 2020) gauged participants' (n = 3707) level of nutrition-health misinformation discernment by presenting them with 25 statements (including unfounded or unproven claims circulated at the time), alongside the influence of information sources of varying quality on the frequency of changes in their eating behavior and the extent of misinformation held, depending on the source used for such changes. Results revealed widespread misinformation about food, eating, and health practices related to COVID-19, with the 25 statements put to participants receiving up to 43% misinformed answers (e.g., 'It is safe to eat fruits and vegetables that have been washed with soap or diluted bleach'). Whereas higher quality information sources (nutrition scientists, nutrition professionals) had the biggest influence on eating behavior change, we found greater misinformation susceptibility when relying on poor quality sources for changing diet. Appropriate discernment of misinformation was weakest amongst participants who more frequently changed their eating behavior because of information from poor quality sources, suggesting disparities in the health risks/safety of the changes performed.

Machine learning-based social media bot detection: a comprehensive literature review

In today’s digitalized era, Online Social Networking platforms are growing to be a vital aspect of each individual’s daily life. The availability of the vast amount of information and their open nature attracts the interest of cybercriminals to create malicious bots. Malicious bots in these platforms are automated or semi-automated entities used in nefarious ways while simulating human behavior. Moreover, such bots pose serious cyber threats and security concerns to society and public opinion. They are used to exploit vulnerabilities for illicit benefits such as spamming, fake profiles, spreading inappropriate/false content, click farming, hashtag hijacking, and much more. Cybercriminals and researchers are always engaged in an arms race as new and updated bots are created to thwart ever-evolving detection technologies. This literature review attempts to compile and compare the most recent advancements in Machine Learning-based techniques for the detection and classification of bots on five primary social media platforms namely Facebook, Instagram, LinkedIn, Twitter, and Weibo. We bring forth a concise overview of all the supervised, semi-supervised, and unsupervised methods, along with the details of the datasets provided by the researchers. Additionally, we provide a thorough breakdown of the extracted feature categories. Furthermore, this study also showcases a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to explore.

Identification of Bots and Cyborgs in the #FeesMustFall Campaign

Bots (social robots) are computer programs that replicate human behavior in online social networks. They are either fully automated or semi-automated, and their use makes online activism vulnerable to manipulation. This study examines the existence of social robots in the #FeesMustFall movement by conducting a scientific investigation into whether social bots were present in the form of Twitter bots and cyborgs. A total of 576,823 tweets posted between 15 October 2015 and 10 April 2017 were cleaned, with 490,449 tweets analyzed for 90,783 unique persons. Three separate approaches were used to screen out suspicious bot and cyborg activity, supplemented by the DeBot team’s methodology. User 1 and User 2, two of the 90,783 individuals, were recognized as bots or cyborgs in the study and contributed 22,413 (4.57 percent) of the 490,449 tweets. This confirms the existence of bots throughout the campaign, which aided in the #FeesMustFall’s amplification on Twitter, complicating sentiment analysis and invariably making it the most popular and lengthiest hashtag campaign in Africa, particularly at the time of data collection.