Judges are not the first political officials that come to mind when one considers the role of social media in modern politics. Following in the wake of some prominent judicial personalities adopting Twitter, however, a growing number of state high court judges have adopted and established more public personas on the platform. Judges use Twitter in substantively different ways than traditional elected officials (Curry and Fix 2019); however, little is understood about how the use of such social media platforms affects broader judicial networks. Recognizing that judges, like typical social media users, may aspire to expand their networks to build and appeal to broader audiences, we contend that active participation in judicial Twitterverse could yield personal and professional advantages. Here, we address a currently unexplored question: To what extent have judges formed a distinctive “judicial network,” on Twitter, and what discernible patterns present in these networks? Leveraging the unique structure of social media, we collect comprehensive network data on judging using Twitter and analyze what institutional and social factors impact greater power within the judicial network. We find that early adoption, electoral concerns, and connective links between judges all impact the strength of the judicial network, highlighting the complex motivations driving judicial Twitter engagement, and the significance of network building in judges’ social media strategies and its potential impact on career advancement.

The proliferation of social networks has caused an increase in the amount of textual content generated by users. The voluminous nature of such content poses a challenge to users, necessitating the development of technological solutions for automatic summarisation. This paper presents a two-stage framework for generating abstractive summaries from a collection of Twitter texts. In the first stage of the framework, event detection is carried out through clustering, followed by event summarisation in the second stage. Our approach involves generating contextualised vector representations of tweets and applying various clustering techniques to the vectors. The quality of the resulting clusters is evaluated, and the best clusters are selected for the summarisation task based on this evaluation. In contrast to previous studies, we experimented with various clustering techniques as a preprocessing step to obtain better event representations. For the summarisation task, we utilised pre-trained models of three state-of-the-art deep neural network architectures and evaluated their performance on abstractive summarisation of the event clusters. Summaries are generated from clusters that contain (a) unranked tweets, (b) all ranked tweets, and (c) the top 10 ranked tweets. Of these three sets of clusters, we obtained the best ROUGE scores from the top 10 ranked tweets. From the summaries generated from the clusters containing the top ten tweets, we obtained ROUGE-1 F score of 48%, ROUGE-2 F score of 37%, ROUGE-L F score of 44%, and ROUGE-SU F score of 33% which suggests that if relevant tweets are at the top of a cluster, and then better summaries are generated.

Polarizing rhetoric and negative tone are thought to generate more attention on social media. We seek to describe and analyze how presidential candidates in Colombia’s 2022 election deployed (de)polarizing rhetoric and tone, around what topics, and with what effects. We analyze the tweets (and corresponding engagement) of the four leading candidates during the campaign. Tone behaves as expected. Negatively worded tweets receive overall more likes and retweets, though the strength of their effect varies by candidate. Polarizing rhetoric behaves differently. Using polarizing and depolarizing rhetoric proved better than neutral messages, but using depolarizing rhetoric, generated greater engagement than its polarizing counterpart. This study suggests that the visibility of a candidate does not necessarily correspond to their greater use of Twitter, an increased deployment of polarizing rhetoric, or an emphasis on negative emotions. This article provides a glimmer of hope regarding the potential usefulness of positive uniting messages on Twitter (now X).

Used by politicians, journalists, and citizens, Twitter has been the most important social media platform to investigate political phenomena such as hate speech, polarization, or terrorism for over a decade. A high proportion of Twitter studies of emotionally charged or controversial content limit their ability to replicate findings due to incomplete Twitter-related replication data and the inability to recrawl their datasets entirely. This paper shows that these Twitter studies and their findings are considerably affected by nonrandom tweet mortality and data access restrictions imposed by the platform. While sensitive datasets suffer a notably higher removal rate than nonsensitive datasets, attempting to replicate key findings of Kim’s (2023, Political Science Research and Methods 11, 673–695) influential study on the content of violent tweets leads to significantly different results. The results highlight that access to complete replication data is particularly important in light of dynamically changing social media research conditions. Thus, the study raises concerns and potential solutions about the broader implications of nonrandom tweet mortality for future social media research on Twitter and similar platforms.

This article analyzes tweets in the Turkish language from November 2020 to May 2021 in which Kurds are explicitly mentioned that feature negative animalization directed toward Kurds and pro-Kurdish organizations. It systematically compares ways of animalization attribution, to what entities the animalization is attributed mostly, and the attributors (actors) of animalization. First, it argues that animalizing dehumanization directed at Kurds in the data set principally occurs for attributing the lack of four human traits: agency, civility, morality, and rationality. Second, it shows in what different ways the lack of these traits is attributed to Kurdish people in general and to major pro-Kurdish groups such as HDP (the largest pro-Kurdish legal political party) and PKK (the largest pro-Kurdish armed group). Finally, it discloses three main political networks among Twitter users within the data set and characterizes how negative animal references to Kurds, pro-Kurdish groups, and each other were used by these actors. Thus, this research seeks to establish a framework to study other ethnic conflicts from the perspective of animalization and invites further research on whether the trends that were found imply a general tendency around the world.

In this article, we document the gender of the noun “COVID-19” in a database of more than 76,000 tweets and in traditional media (approximately 500,000 articles) in French as spoken in Africa, (North) America and Europe. We find that North American media comply near-categorically with the recommendations of the feminine by the World Health Organization and local linguistic authorities in March 2020. The majority of North American tweets follow suit soon after. The African data show an increase of articles and tweets adopting the feminine after the Académie française's recommendation in May 2020. Finally, the feminine is negligible in the European data. We argue that among the factors at play are dialect-specific differences in French gender and loanword adaptation; the complex relationship among linguistic authorities, the public, and local media; and the relative delay in the Académie française's recommendation of the feminine.

How does protest affect political speech? Protest is an important form of political claim-making, yet our understanding of its influence on how individual legislators communicate remains limited. Our paper thus extends a theoretical framework on protests as information about voter preferences, and evaluates it using crowd-sourced protest data from the 2017–2019 Fridays for Future protests in the UK. We combine these data with ~2.4m tweets from 553 legislators over this period and text data from ~150k parliamentary speech records. We find that local protests prompted MPs to speak more about the climate, but only online. These results demonstrate that protest can shape the timing and substance of political communication by individual elected representatives. They also highlight an important difference between legislators' offline and online speech, suggesting that more work is needed to understand how political strategies differ across these arenas.

Two Twitter-based corpus studies are reported to account for the increasing preference in The Netherlands for the stigmatized subject use of the object pronoun hun ‘them.’ Twitter data were collected to obtain a sufficient number of hun-tokens, but also to investigate the validity of two hypotheses on the preference for hun, this is, that subject-hun is a contrast profiler which thrives in contexts of evaluation and qualification, and that subject-hun is propelled by its dynamic social meaning, being a tool for nonposh and streetwise self-stylization. Although the latter is not normally a predictor included in regression analyses of constructional choice, it turns out that expressively spruced up tweets with vivid contrast profiling are the prime biotope of subject-hun. Along the way, this paper reviews the potential of Twitter data for the reconciliation of macro-big-data analysis with micro-sociolinguistic focus, but it also reports and attempts to remedy three concerns.

Politicians carefully construct a public persona that is authentic to who they are as individuals but also addresses voter expectations. Many Black candidates follow a deracialization strategy in which they downplay their racial identities to seek voter support while some follow a racial distinction strategy in which they highlight their racial identities but situate them within hegemonic national narratives. But questions remain about whether a candidate’s decision to use one strategy over another is shaped by national context, partisanship, political position, and riding competitiveness. This paper thus asks the question: How do Black candidates in Canadian elections deploy race in their campaign communications, and what factors might explain any differences in their strategies? To answer this question, we analyze how Black candidates used Twitter during the 2021 Canadian election. Our analysis reveals that Black candidates generally used a deracialization strategy when communicating on Twitter, opting to celebrate the many cultural groups in their riding rather than casting their appeal only to Black voters. They only highlighted their racial identities or racial issues when world or campaign events gave them the political cover to do so. But the degree to which Black candidates engaged in (de)racialized communications differed by party.

Depuis l'annonce du rachat de Twitter par Elon Musk, les risques quant à l'avenir du réseau social laissent entrevoir l'avènement d'un « Frankenstein 2.0 » – une notion qui fait référence aux grandes entreprises de technologie dont le contrôle échappe à leurs créateurs. Derrière cet événement se pose la question des dérives technologiques et de leurs impacts sur la société. C'est une occasion pour les gouvernements et en particulier celui du Canada, qui encourage l'ouverture des données et les politiques en faveur des technologies, de redéfinir les règles du jeu. En ce sens, quelques recommandations sont proposées en vue de consolider l'encadrement normatif des technologies au Canada. La loi sur l'intelligence artificielle et les données (LIAD), actuellement en discussion au sein du Parlement, devrait en effet aller plus loin concernant la transparence des systèmes automatisés et renforcer ses exigences en matière de gouvernance des données « algorithmiques ».