Maël Kubli

Hate speech is widely seen as a significant obstacle to constructive online discourse, but the most effective strategies to mitigate its effects remain unclear. We claim that understanding its distribution across users is key to developing and evaluating effective content moderation strategies. We address this missing link by first examining the distribution of hate speech in five original datasets that collect user-generated posts across multiple platforms (social media and online newspapers) and countries (Switzerland and the United States). Across these diverse samples, the vast majority of hate speech is produced by a small fraction of users. Second, results from a pre-registered field experiment on Twitter indicate that counterspeech strategies obtain only small reductions of future hate speech, mainly because this approach proves ineffective against the most prolific contributors of hate. These findings suggest that complementary content moderation strategies may be necessary to effectively address the problem.

An unresolved aspect of women's underrepresentation in politics is the media portrayal of female candidates. This paper studies how advocacy campaigns may affect potential bias, leveraging the 2019 Swiss federal elections, which were shaped by two nation-wide, cross-party campaigns advocating for gender equality. The empirical analysis compares the 2015 and 2019 election campaigns, relying on an original dataset of the mentions that all candidates (over 3,700 respectively 4,600) received in over 2.2 million news articles. The analysis produces three main results. First, although in both elections male candidates received more media attention than female candidates did, the gender gap was significantly smaller in 2019 than in 2015. Second, in both elections, male and female candidates tended to be mentioned in conjunction with gender-stereotypical topics. Third, the gender gap in media attention before and after a key women's rights event was similar to that between the corresponding periods in 2015. These findings suggest that the differences observed between 2015 and 2019 are linked to the political campaign at large rather than to a specific event, despite its historical dimensions. The results contribute to the understanding of how advocacy campaigns can change bias in media coverage and, methodologically, to measuring and understanding gendered media coverage of politics.

Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers' activities on Twitter. However, the extent to which Twitter crypto influencer's posts about trading signals and their effect on market prices is mostly unexplored. In this paper, we use LLMs to uncover buy and not-buy signals from influencers and news outlets' Twitter posts and use a VAR analysis with Granger Causality tests and cross-correlation analysis to understand how these trading signals are temporally correlated with the top nine major cryptocurrencies' prices. Overall, the results show a mixed pattern across cryptocurrencies and temporal periods. However, we found that for the top three cryptocurrencies with the highest presence within news and influencer posts, their aggregated LLM-detected trading signal over the preceding 24 hours granger-causes fluctuations in their market prices, exhibiting a lag of at least 6 hours. In addition, the results reveal fundamental differences in how influencers and news outlets cover cryptocurrencies.

Many NLP applications require manual text annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using four samples of tweets and news articles (n= 6,183), we show that ChatGPT outperforms crowd workers for several annotation tasks, including relevance, stance, topics, and frame detection. Across the four datasets, the zero-shot accuracy of ChatGPT exceeds that of crowd workers by about 25 percentage points on average, while ChatGPT’s intercoder agreement exceeds that of both crowd workers and trained annotators for all tasks. Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk. These results demonstrate the potential of large language models to drastically increase the efficiency of text classification.

The 2019 Swiss national elections were characterized by the unusual prominence of two issues, environment and gender, whereas two staples of Swiss politics, immigration and Europe, were less dominant compared to previous elections. We study how, in this context, the media and party agenda were linked to issue ownership. Specifically, we consider whether political parties that own an issue could lead the media agenda and the agenda of other parties. Our analysis relies on all tweets and press releases of major Swiss political parties from January to October 2019 and 37,225 newspaper articles published during the same period. Results show, first, that the agenda‐setting capacity of parties was restricted to the gender issue, and second, that the link between issue ownership and agenda setting is ambiguous. These findings suggest that during election campaigns, agenda setting may be largely exogenous to both parties and media.

The relationship between digital technology and politics is an important phenomenon that remains poorly understood due to several structural problems. A key issue is the lack of adequate research infrastructures or the lack of access. This article discusses the challenges many social scientists face and presents the infrastructure we built in Switzerland to overcome them, using COVID-19 as an example. We conclude by discussing seven lessons we learned: automatization is key; avoid data hoarding; outsource some parts of the infrastructure but not others; focus on substantive questions; share data in the context of collaborations; engage in targeted public outreach; and collaboration is more promising than competition. We hope that our experience is helpful to other researchers pursuing similar goals.

We study the role of social media in debates regarding two policy responses to COVID‐19 in Switzerland: face‐mask rules and contact‐tracing apps. We use a dictionary classifier to categorize 612'177 tweets by parties, politicians, and the public as well as 441'458 articles published in 76 newspapers between February and August 2020. We distinguish between “problem” (COVID‐19) and “solutions” (face masks and contact‐tracing apps) and, using a vector autoregression approach, we analyze the relationship between their salience on social and traditional media, as well as among different groups on social media. We find that overall attention to COVID‐19 was not driven by endogenous dynamics between the different actors. By contrast, the debate on face masks was led by the attentive public and by politicians, whereas parties and newspapers followed. The results illustrate how social media challenge the capacity of party and media elites to craft a consensus regarding the appropriateness of different measures as responses to a major crisis.

What is the role of social media in political agenda setting? Digital platforms have reduced the gatekeeping power of traditional media and, potentially, they have increased the capacity of various kinds of actors to shape the agenda. We study this question in the Swiss context by examining the connections between three agendas: the traditional media agenda, the social media agenda of parties, and the social media agenda of politicians. Specifically, we validate and apply supervised machine learning classifiers to categorize 2.78 million articles published in 84 newspapers, 6,500 tweets posted on official party accounts, and 210,000 tweets posted by politicians on their own accounts from January 2018 until December 2019. We first use the classifier to measure the salience of the four most relevant issues of the period: the environment, Europe, gender equality, and immigration. Then, using a vector autoregression (VAR) approach, we analyze the relationship between the three agendas. Results show that not only do the traditional media agenda, the social media agenda of parties, and the social media agenda of politicians influence one another but, overall, no agenda leads the others more than it is led by them. There is one important exception: for the environment issue, the social media agenda of parties is more predictive of the traditional media agenda than vice-versa. These findings underscore how closely different agendas are tied together, but also show that advocacy campaigns may play an important role in both constraining and enabling parties to push their specific agendas.