arXiv subjects

Dominik Bär

Publications and source records attributed to Dominik Bär.

9 recordsLinked to original sources

arXiv2026

Gender-based discrepancies in the algorithmic delivery of political ads on social media

Social media has become a key channel for political advertising during election campaigns. However, algorithmic biases in the delivery of these ads may distort the public's exposure to political messaging. This can hinder citizens' ability to make informed choices and undermine equal access to political discourse, raising concerns about the integrity of electoral processes. In this study, we examine gender-based discrimination in the delivery of political ads during the 2024 European Parliament elections. Using a large-scale dataset of over 110000 ads from 453 political parties and 968 candidates that generated over 7 billion impressions across 25 EU countries, we find that men were significantly more likely to be shown ads from populist and far-right parties than women -- even after accounting for ad content, platform-level competition, and targeting strategies. All else equal, ads by populist parties reach, on average, a 6 percentage point higher male share. Such imbalances restrict the ability of parties to reach diverse audiences and prevent voters from engaging equally with the full range of political viewpoints. This pattern is particularly concerning given that far-right and populist ads may reinforce political polarization and widen existing gender gaps in political engagement. Our findings underscore the need for platforms and policymakers to audit algorithmic ad delivery in political campaigns on social media and to implement safeguards that ensure fairness and protect democratic processes.

cs.CY↗

arXiv2024

Generative AI may backfire for counterspeech

Online hate speech poses a serious threat to individual well-being and societal cohesion. A promising solution to curb online hate speech is counterspeech. Counterspeech is aimed at encouraging users to reconsider hateful posts by direct replies. However, current methods lack scalability due to the need for human intervention or fail to adapt to the specific context of the post. A potential remedy is the use of generative AI, specifically large language models (LLMs), to write tailored counterspeech messages. In this paper, we analyze whether contextualized counterspeech generated by state-of-the-art LLMs is effective in curbing online hate speech. To do so, we conducted a large-scale, pre-registered field experiment (N=2,664) on the social media platform Twitter/X. Our experiment followed a 2x2 between-subjects design and, additionally, a control condition with no counterspeech. On the one hand, users posting hateful content on Twitter/X were randomly assigned to receive either (a) contextualized counterspeech or (b) non-contextualized counterspeech. Here, the former is generated through LLMs, while the latter relies on predefined, generic messages. On the other hand, we tested two counterspeech strategies: (a) promoting empathy and (b) warning about the consequences of online misbehavior. We then measured whether users deleted their initial hateful posts and whether their behavior changed after the counterspeech intervention (e.g., whether users adopted a less toxic language). We find that non-contextualized counterspeech employing a warning-of-consequence strategy significantly reduces online hate speech. However, contextualized counterspeech generated by LLMs proves ineffective and may even backfire.

cs.SI↗

arXiv2024

HQP: A Human-Annotated Dataset for Detecting Online Propaganda

Online propaganda poses a severe threat to the integrity of societies. However, existing datasets for detecting online propaganda have a key limitation: they were annotated using weak labels that can be noisy and even incorrect. To address this limitation, our work makes the following contributions: (1) We present HQP: a novel dataset (N = 30,000) for detecting online propaganda with high-quality labels. To the best of our knowledge, HQP is the first large-scale dataset for detecting online propaganda that was created through human annotation. (2) We show empirically that state-of-the-art language models fail in detecting online propaganda when trained with weak labels (AUC: 64.03). In contrast, state-of-the-art language models can accurately detect online propaganda when trained with our high-quality labels (AUC: 92.25), which is an improvement of ~44%. (3) We show that prompt-based learning using a small sample of high-quality labels can still achieve a reasonable performance (AUC: 80.27) while significantly reducing the cost of labeling. (4) We extend HQP to HQP+ to test how well propaganda across different contexts can be detected. Crucially, our work highlights the importance of high-quality labels for sensitive NLP tasks such as propaganda detection.

cs.CL↗

arXiv2024

Systematic discrepancies in the delivery of political ads on Facebook and Instagram

Political advertising on social media has become a central element in election campaigns. However, granular information about political advertising on social media was previously unavailable, thus raising concerns regarding fairness, accountability, and transparency in the electoral process. In this paper, we analyze targeted political advertising on social media via a unique, large-scale dataset of over 80000 political ads from Meta during the 2021 German federal election, with more than 1.1 billion impressions. For each political ad, our dataset records granular information about targeting strategies, spending, and actual impressions. We then study (i) the prevalence of targeted ads across the political spectrum; (ii) the discrepancies between targeted and actual audiences due to algorithmic ad delivery; and (iii) which targeting strategies on social media attain a wide reach at low cost. We find that targeted ads are prevalent across the entire political spectrum. Moreover, there are considerable discrepancies between targeted and actual audiences, and systematic differences in the reach of political ads (in impressions-per-EUR) among parties, where the algorithm favors ads from populists over others.

cs.SI↗

arXiv2023

Russian propaganda on social media during the 2022 invasion of Ukraine

The Russian invasion of Ukraine in February 2022 was accompanied by practices of information warfare, yet existing evidence is largely anecdotal while large-scale empirical evidence is lacking. Here, we analyze the spread of pro-Russian support on social media. For this, we collected N = 349,455 messages from Twitter with pro-Russian support. Our findings suggest that pro-Russian messages received ~251,000 retweets and thereby reached around 14.4 million users. We further provide evidence that bots played a disproportionate role in the dissemination of pro-Russian messages and amplified its proliferation in early-stage diffusion. Countries that abstained from voting on the United Nations Resolution ES-11/1 such as India, South Africa, and Pakistan showed pronounced activity of bots. Overall, 20.28% of the spreaders are classified as bots, most of which were created at the beginning of the invasion. Together, our findings suggest the presence of a large-scale Russian propaganda campaign on social media and highlight the new threats to society that originate from it. Our results also suggest that curbing bots may be an effective strategy to mitigate such campaigns.

cs.SI↗

arXiv2023

Finding Qs: Profiling QAnon Supporters on Parler

The social media platform "Parler" has emerged into a prominent fringe community where a significant part of the user base are self-reported supporters of QAnon, a far-right conspiracy theory alleging that a cabal of elites controls global politics. QAnon is considered to have had an influential role in the public discourse during the 2020 U.S. presidential election. However, little is known about QAnon supporters on Parler and what sets them aside from other users. Building up on social identity theory, we aim at profiling the characteristics of QAnon supporters on Parler. We analyze a large-scale dataset with more than 600,000 profiles of English-speaking users on Parler. Based on users' profiles, posts, and comments, we then extract a comprehensive set of user features, linguistic features, network features, and content features. This allows us to perform user profiling and understand to what extent these features discriminate between QAnon and non-QAnon supporters on Parler. Our analysis is three-fold: (1) We quantify the number of QAnon supporters on Parler, finding that 34,913 users (5.5% of all users) openly report to support the conspiracy. (2) We examine differences between QAnon vs. non-QAnon supporters. We find that QAnon supporters differ statistically significantly from non-QAnon supporters across multiple dimensions. For example, they have, on average, a larger number of followers, followees, and posts, and thus have a large impact on the Parler network. (3) We use machine learning to identify which user characteristics discriminate QAnon from non-QAnon supporters. We find that user features, linguistic features, network features, and content features, can - to a large extent - discriminate QAnon vs. non-QAnon supporters on Parler. In particular, we find that user features are highly discriminatory, followed by content features and linguistic features.

cs.SI↗

arXiv2023

Analyzing Social Media Activities at Bellingcat

Open-source journalism emerged as a new phenomenon in the media ecosystem, which uses crowdsourcing to fact-check and generate investigative reports for world events using open sources (e.g., social media). A particularly prominent example is Bellingcat. Bellingcat is known for its investigations on the illegal use of chemical weapons during the Syrian war, the Russian responsibility for downing flight MH17, the identification of the perpetrators in the attempted murder of Alexei Navalny, and war crimes in the Russo-Ukraine war. Crucial for this is social media in order to disseminate findings and crowdsource fact-checks. In this work, we characterize the social media activities at Bellingcat on Twitter. For this, we built a comprehensive dataset of all N=24,682 tweets posted by Bellingcat on Twitter since its inception in July 2014. Our analysis is three-fold: (1) We analyze how Bellingcat uses Twitter to disseminate information and collect information from its follower base. Here, we find a steady increase in both posts and replies over time, particularly during the Russo-Ukrainian war, which is in line with the growing importance of Bellingcat for the traditional media ecosystem. (2) We identify characteristics of posts that are successful in eliciting user engagement. User engagement is particularly large for posts embedding additional media items and with a more negative sentiment. (3) We examine how the follower base has responded to the Russian invasion of Ukraine. Here, we find that the sentiment has become more polarized and negative. We attribute this to a ~13-fold increase in bots interacting with the Bellingcat account. Overall, our findings provide recommendations for how open-source journalism such as Bellingcat can successfully operate on social media.

cs.SI↗

arXiv2023

New threats to society from free-speech social media platforms

In recent years, several free-speech social media platforms (so-called "alt-techs") have emerged, such as Parler, Gab, and Telegram. These platforms market themselves as alternatives to mainstream social media and proclaim "free-speech" due to the absence of content moderation, which has been attracting a large base of partisan users, extremists, and supporters of conspiracy theories. In this comment, we discuss some of the threats that emerge from such social media platforms and call for more policy efforts directed at understanding and countering the risks for society.

cs.SI↗

arXiv2022

Online Emotions During the Storming of the U.S. Capitol: Evidence from the Social Media Network Parler

The storming of the U.S. Capitol on January 6, 2021 has led to the killing of 5 people and is widely regarded as an attack on democracy. The storming was largely coordinated through social media networks such as Parler. Yet little is known regarding how users interacted on Parler during the storming of the Capitol. In this work, we examine the emotion dynamics on Parler during the storming with regard to heterogeneity across time and users. For this, we segment the user base into different groups (e.g., Trump supporters and QAnon supporters). We use affective computing (Kratzwald et al. 2018) to infer the emotions in the contents, thereby allowing us to provide a comprehensive assessment of online emotions. Our evaluation is based on a large-scale dataset from Parler, comprising of 717,300 posts from 144,003 users. We find that the user base responded to the storming of the Capitol with an overall negative sentiment. Akin to this, Trump supporters also expressed a negative sentiment and high levels of unbelief. In contrast to that, QAnon supporters did not express a more negative sentiment during the storming. We further provide a cross-platform analysis and compare the emotion dynamics on Parler and Twitter. Our findings point at a comparatively less negative response to the incidents on Parler compared to Twitter accompanied by higher levels of disapproval and outrage. Our contribution to research is three-fold: (1) We identify online emotions that were characteristic of the storming; (2) we assess emotion dynamics across different user groups on Parler; (3) we compare the emotion dynamics on Parler and Twitter. Thereby, our work offers important implications for actively managing online emotions to prevent similar incidents in the future.

cs.SI↗