How to Hijack Twitter: Online Polarisation Strategies of Germany's Political Far-Right
Authors: Philipp Darius, Fabian Stephany
Abstract
With a network approach, we examine the case of the German far-right party Alternative f\"ur Deutschland (AfD) and their potential use of a "hashjacking" strategy. Our findings suggest that right-wing politicians (and their supporters/retweeters) actively and effectively polarise the discourse not just by using their own party hashtags, but also by "hashjacking" the political party hashtags of other established parties. The results underline the necessity to understand the success of right-wing parties, online and in elections, not entirely as a result of external effects (e.g. migration), but as a direct consequence of their digital political communication strategy.
The papers around it, by date and closeness
Higher is closer to this paper; left is older. Lines join papers close to each other: groups of lines are sub-topics. Select a point to see why it is there. Back to the summary of this paper
Papers around this one
Ranked by how close their title and abstract are to this paper’s. Select some to read them together.
Closest papers
-
Catching up with trends: The changing landscape of political discussions on twitter in 2014 and 2019Avinash Tulasi, Kanay Gupta, Omkar Gurjar et al. · 2019 · Social and Information Networks · Not summarized yet
“Catching up with trends” examines political discussions and party activity on Twitter, while this paper focuses on AfD hashtag hijacking as a polarisation strategy.
Abstract
The advent of 4G increased the usage of internet in India, which took a huge number of discussions online. Online Social Networks (OSNs) are the center of these discussions. During elections, political discussions constitute a significant portion of the trending topics on these networks. Politicians and political parties catch up with these trends, and social media then becomes a part of their publicity agenda. We cannot ignore this trend in any election, be it the U.S, Germany, France, or India. Twitter is a major platform where we observe these trends. In this work, we examine the magnitude of political discussions on twitter by contrasting the platform usage on levels like gender, politic…
-
The Russian invasion of Ukraine selectively depolarized the Finnish NATO discussionYan Xia, Antti Gronow, Arttu Malkamäki et al. · 2022 · Social and Information Networks · AI summary available
“The Russian invasion of Ukraine selectively depolarized the Finnish NATO discussion” studies Twitter polarisation dynamics, but focuses on depolarisation after an external threat.
Abstract
It is often thought, yet rarely observed, that an external threat increases the internal cohesion of a nation, and thus decreases polarization. We examine this proposition by analyzing NATO discussion dynamics on Finnish social media following the Russian invasion of Ukraine in February 2022. In Finland, public opinion on NATO had long been polarized along the left-right partisan axis, but the invasion led to a rapid convergence of the opinion, and eventually led the country to apply for NATO membership. We investigate how this depolarization took place among polarized actors on Finnish Twitter. By analyzing retweeting patterns, we find three separated user groups before the invasion: a pro-…
-
The Virality of Hate Speech on Social MediaAbdurahman Maarouf, Nicolas Pröllochs, Stefan Feuerriegel · 2022 · Social and Information Networks · AI summary available
“The Virality of Hate Speech on Social Media” analyzes Twitter retweet cascades and content spread, whereas this paper examines partisan hashtag use and polarisation.
Abstract
Online hate speech is responsible for violent attacks such as, e.g., the Pittsburgh synagogue shooting in 2018, thereby posing a significant threat to vulnerable groups and society in general. However, little is known about what makes hate speech on social media go viral. In this paper, we collected N = 25,219 Twitter cascades with 65,946 retweets and classify them as hateful vs. normal. Using a generalized linear regression, we then estimate differences in the spread of hateful vs. normal content based on author and content variables. We thereby identify important determinants that explain differences in the spreading of hateful vs. normal content. For example, hateful content authored by v…
-
Social Politics: Agenda Setting and Political Communication on Social MediaXinxin Yang, Bo-Chiuan Chen, Mrinmoy Maity et al. · 2016 · International Conference on Social Informatics (pp. 330-344). Springer. 2016 · AI summary available
“Social Politics” studies politicians’ Twitter agendas, while this paper investigates how German far-right actors use rival parties’ hashtags to polarise discourse.
Abstract
Social media play an increasingly important role in political communication. Various studies investigated how individuals adopt social media for political discussion, to share their views about politics and policy, or to mobilize and protest against social issues. Yet, little attention has been devoted to the main actors of political discussions: the politicians. In this paper, we explore the topics of discussion of U.S. President Obama and the 50 U.S. State Governors using Twitter data and agenda-setting theory as a tool to describe the patterns of daily political discussion, uncovering the main topics of attention and interest of these actors. We examine over one hundred thousand tweets pr…
-
Spread of hate speech in online social mediaBinny Mathew, Ritam Dutt, Pawan Goyal et al. · 2018 · Social and Information Networks · AI summary available
“Spread of hate speech in online social media” measures hateful content diffusion on Gab, whereas this paper studies AfD hashtag hijacking on Twitter.
Abstract
The present online social media platform is afflicted with several issues, with hate speech being on the predominant forefront. The prevalence of online hate speech has fueled horrific real-world hate-crime such as the mass-genocide of Rohingya Muslims, communal violence in Colombo and the recent massacre in the Pittsburgh synagogue. Consequently, It is imperative to understand the diffusion of such hateful content in an online setting. We conduct the first study that analyses the flow and dynamics of posts generated by hateful and non-hateful users on Gab (gab.com) over a massive dataset of 341K users and 21M posts. Our observations confirms that hateful content diffuse farther, wider and f…
-
Israel-Hamas War on X: A Case Study of Coordinated Campaigns and Information IntegrityTuğrulcan Elmas, Filipi Nascimento Silva, Manita Pote et al. · 2026 · Social and Information Networks · AI summary available
“Israel-Hamas War on X” identifies coordinated campaigns and their information effects, while this paper focuses on AfD’s hashtag-based polarisation strategy.
Abstract
Coordinated campaigns on social media play a critical role in shaping crisis information environments, particularly during the onset of conflicts when uncertainty is high and verified information is scarce. We study the interplay between coordinated campaigns and information integrity through a case study of the 2023 Israel-Hamas War on Twitter (X). We analyze 4.5~million tweets and employ established coordination detection methods to identify 11 coordinated groups involving 541 accounts. We characterize these groups through a multimodal analysis that includes topics, account amplification, toxicity, emotional tone, visual themes, and misleading claims. Our analysis reveal that coordinated c…
-
Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On RedditHans W. A. Hanley, Deepak Kumar, Zakir Durumeric · 2022 · Social and Information Networks · AI summary available
“Happenstance” tracks Russian state-media narratives on Reddit, a neighbouring study of online political influence rather than AfD hashtag hijacking.
Abstract
In the buildup to and in the weeks following the Russian Federation's invasion of Ukraine, Russian state media outlets output torrents of misleading and outright false information. In this work, we study this coordinated information campaign in order to understand the most prominent state media narratives touted by the Russian government to English-speaking audiences. To do this, we first perform sentence-level topic analysis using the large-language model MPNet on articles published by ten different pro-Russian propaganda websites including the new Russian "fact-checking" website waronfakes.com. Within this ecosystem, we show that smaller websites like katehon.com were highly effective at p…
-
When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit BanMilo Z. Trujillo, Samuel F. Rosenblatt, Guillermo de Anda Jáuregui et al. · 2021 · Social and Information Networks · AI summary available
“When the Echo Chamber Shatters” tracks community language after Reddit bans, while this paper examines political polarisation through hashtags on Twitter.
Abstract
Community-level bans are a common tool against groups that enable online harassment and harmful speech. Unfortunately, the efficacy of community bans has only been partially studied and with mixed results. Here, we provide a flexible unsupervised methodology to identify in-group language and track user activity on Reddit both before and after the ban of a community (subreddit). We use a simple word frequency divergence to identify uncommon words overrepresented in a given community, not as a proxy for harmful speech but as a linguistic signature of the community. We apply our method to 15 banned subreddits, and find that community response is heterogeneous between subreddits and between user…
Go further
How are these papers found?
We compare the title and abstract of this paper with those of every paper summarized on this site, and with the papers Semantic Scholar recommends, those it cites and those citing it (for a much-cited paper, its most recent citations and, from OpenAlex, the most cited ones). Among the prior work and the follow-ups, the papers that use each other in depth, a method it uses and the much-cited follow-ups come a little higher. The levels (very close, close, related) come from the similarity of the texts, not from a judgment of quality; * marks a paper without an abstract, compared by its title. The sentence under a paper is written by an AI from the two abstracts: check it in the papers. A paper marked not relevant by three readers leaves the list.
For now only among the papers summarized on this site: search again to add the prior work, the follow-ups and Semantic Scholar’s recommendations.