Emotions and virality: Social transmission of political messages on Twitter

Clicks and tricks: The dark art of online persuasion

Internet users are inundated with attempts to persuade, including digital nudges like defaults, friction, and reinforcement. When these nudges fail to be transparent, optional, and beneficial, they can become 'dark patterns', categorised here under the acronym FORCES (Frame, Obstruct, Ruse, Compel, Entangle, Seduce). Elsewhere, psychological principles like negativity bias, the curiosity gap, and fluency are exploited to make social content viral, while more covert tactics including astroturfing, meta-nudging, and inoculation are used to manufacture consensus. The power of these techniques is set to increase in line with technological advances such as predictive algorithms, generative AI, and virtual reality. Digital nudges can be used for altruistic purposes including protection against manipulation, but behavioural interventions have mixed effects at best.

Data and models for stance and premise detection in COVID-19 tweets: Insights from the Social Media Mining for Health (SMM4H) 2022 shared task

The COVID-19 pandemic has sparked numerous discussions on social media platforms, with users sharing their views on topics such as mask-wearing and vaccination. To facilitate the evaluation of neural models for stance detection and premise classification, we organized the Social Media Mining for Health (SMM4H) 2022 Shared Task 2. This competition utilized manually annotated posts on three COVID-19-related topics: school closures, stay-at-home orders, and wearing masks. In this paper, we extend the previous work and present newly collected data on vaccination from Twitter to assess the performance of models on a different topic. To enhance the accuracy and effectiveness of our evaluation, we employed various strategies to aggregate tweet texts with claims, including models with feature-level (early) fusion and dual-view architectures from the SMM4H 2022 Task 2 leaderboard. Our primary objective was to create a valuable dataset and perform an extensive experimental evaluation to support future research in argument mining in the health domain.