Detection and impact estimation of social bots in the Chilean Twitter network.

The rise of bots that mimic human behavior represents one of the most pressing threats to healthy information environments on social media. Many bots are designed to increase the visibility of low-quality content, spread misinformation, and artificially boost the reach of brands and politicians. These bots can also disrupt civic action coordination, such as by flooding a hashtag with spam and undermining political mobilization. Social media platforms have recognized these malicious bots' risks and implemented strict policies and protocols to block automated accounts. However, effective bot detection methods for Spanish are still in their early stages. Many studies and tools used for Spanish are based on English-language models and lack performance evaluations in Spanish. In response to this need, we have developed a method for detecting bots in Spanish called Botcheck. Botcheck was trained on a collection of Spanish-language accounts annotated in Twibot-20, a large-scale dataset featuring thousands of accounts annotated by humans in various languages. We evaluated Botcheck's performance on a large set of labeled accounts and found that it outperforms other competitive methods, including deep learning-based methods. As a case study, we used Botcheck to analyze the 2021 Chilean Presidential elections and discovered evidence of bot account intervention during the electoral term. In addition, we conducted an external validation of the accounts detected by Botcheck in the case study and found our method to be highly effective. We have also observed differences in behavior among the bots that are following the social media accounts of official presidential candidates.

Mapping moral language on US presidential primary campaigns reveals rhetorical networks of political division and unity.

During political campaigns, candidates use rhetoric to advance competing visions and assessments of their country. Research reveals that the moral language used in this rhetoric can significantly influence citizens' political attitudes and behaviors; however, the moral language actually used in the rhetoric of elites during political campaigns remains understudied. Using a data set of every tweet (N=139,412) published by 39 US presidential candidates during the 2016 and 2020 primary elections, we extracted moral language and constructed network models illustrating how candidates' rhetoric is semantically connected. These network models yielded two key discoveries. First, we find that party affiliation clusters can be reconstructed solely based on the moral words used in candidates' rhetoric. Within each party, popular moral values are expressed in highly similar ways, with Democrats emphasizing careful and just treatment of individuals and Republicans emphasizing in-group loyalty and respect for social hierarchies. Second, we illustrate the ways in which outsider candidates like Donald Trump can separate themselves during primaries by using moral rhetoric that differs from their parties' common language. Our findings demonstrate the functional use of strategic moral rhetoric in a campaign context and show that unique methods of text network analysis are broadly applicable to the study of campaigns and social movements.

Mental Psychology of Voters: Social Media Marketing, Corporate Social Responsibility and Political Campaigns Perspectives / Psicología Mental de los Votantes: Marketing en Redes Sociales, Responsabilidad Social Corporativa y Perspectivas de Campañas Políticas

This study examines how the psychological behavior of voters, voter attitudes, and voters’ actions respond to situations involving the use of a series of social media marketing, corporate social responsibility, and political campaign activities. It also investigates the mediating consequences of political campaigns on the mental psychology of voters. Questionnaires were electornically administered among university students in the Kurdistan region of Iraq. In conjunction with SPSS and AMOS version 24, the structural equation modeling approach was utilised to examine the relationships between the modeled variables on the basis of data collected from 525 university students. The reliability and validity of the measurements was confirmed before the proposed research hypotheses were tested using confirmatory factor analysis. The results revealed that social media marketing positively influences political campaigns, corporate social responsibility, and thee mental psychology of voters. CSR, political campaigns, and social media marketing's positive effects on mental psychology of voter have been supported. The results also confirmed that political campaigns significantly mediate the relationship between social media marketing and mental psychology of voters as well as the relationship between CSR and mental psychology. This study enriches the understanding of social media marketing, CSR activities, and political campaigns voter vis-à-vis the mental psychology of university students. Political groups can gain benefits by understanding how specific CSR activities can influence voter’s mental psychology and social media engagement value, leading to enhanced mental psychology and choices. The study's originality and novelty are embedded in its ability to build an integrated model that examines the interaction between social media marketing and mental psychology related to voters among university students.(AU)

Relationship between Drug Overdose Mortality and Coverage of Drug-Related Issues in US Television Political Campaign Advertising in the 2012 and 2016 Election Cycles.

CONTEXT: Understanding the role of drug-related issues in political campaign advertising can provide insight on the salience of this issue and the priorities of candidates for elected office. This study sought to quantify the share of campaign advertising mentioning drugs in the 2012 and 2016 election cycles and to estimate the association between local drug overdose mortality and drug mentions in campaign advertising across US media markets. METHODS: The analysis used descriptive and spatial statistics to examine geographic variation in campaign advertising mentions of drugs across all 210 US media markets, and it used multivariable regression to assess area-level factors associated with that variation. FINDINGS: The share of campaign ads mentioning drugs grew from 0.5% in the 2012 election cycle to 1.6% in the 2016 cycle. In the 2016 cycle, ads airing in media markets with overdose mortality rates in the 95th percentile were more than three times as likely to mention drugs as ads airing in areas with overdose mortality rates in the 5th percentile. CONCLUSIONS: A small proportion of campaign advertising mentioned drug-related issues. In the 2016 cycle, the issue was more prominent in advertising in areas hardest hit by the drug overdose crisis and in advertising for local races.