Victimhood is commonly deemed negative. The dominant account of victimhood argues that leveraging victimhood involves asserting the moral superiority of the weak, leading to an oversimplification of complex political matters into moral binaries of good versus evil. According to this perspective, victimhood traps victims in a perennial position of weakness, thereby diminishing their agency. This paper challenges this negative perspective and argues that victimhood can enhance agency, serving as a positive political resource. When victimhood involves the acknowledgment of inherent vulnerability shared by all individuals, whether they are victims or non-victims, and concerns the unjust distributions of vulnerability experiences, it can empower individuals to overcome excessive self-doubt and transform their victimization into a political agenda. By examining the subway protests organized by Korean Solidarity Against Disability Discrimination activists, I demonstrate how recognizing the agency-enhancing potential of victimhood helps us better understand the political significance of these actions.
We examine in our article how art and cultural activities in rural regions can support social sustainability by fostering local social relations, social cohesion and cultural participation. Our analysis draws on four ethnographic case studies in four different rural areas in Germany. We argue that the impact of art and cultural practices on the social fabric of villages and small towns should not only be examined regarding art and cultural activities in a narrow sense i.e., the activities defined as “artistic” and “cultural”. Instead, it is necessary to consider more comprehensively the actors’ social, economic and political activities as well as the local and material environments connected to them. Drawing on cultural and social practice theory, we analyze four modes of practice of art and cultural work distinguished by different ways of working on the social fabric in rural regions: artist mode, social worker mode, cultural entrepreneur mode and activist mode. The differences observed between these modes of practice and their impacts on social sustainability amount, in part, to different understandings of art and culture held by cultural workers themselves but also unfold regarding the socioeconomic and spatial contexts of the cultural initiatives investigated.
This study aims to determine the mediating role of behavioral brand loyalty in the effect of consumer innovation barriers on online smartphone purchase intention. For this purpose, a field research was conducted with Antalya Belek University students between 15 February and 15 March 2025. In this study, face-to-face survey forms were used to collect 485 data points. The data were analyzed using the Jamovi statistical software. The results of the study indicated that behavioral brand loyalty has a direct and significant effect on online smartphone purchase intention. However, among the consumer innovation barriers, the Usage Barrier, Value Barrier, Risk Barrier, Image Barrier, Pleasure Barrier, and Dominance Barrier were found to have a negative and significant impact on online smartphone purchase intention. Furthermore, the mediating effect of behavioral brand loyalty on the relationship between these barriers and online smartphone purchase intention also yielded negative and significant results.
Aris Setiawan, Zulkarnain Mistortoify, Yuddan Fijar Sugma Timur
This study aims to analyze the use of music in the 2024 Indonesian presidential election. Although the election
was completed on February 15, 2024, the use of music as a campaign tool took place massively. This study tries
to observe how music can influence public opinion to determine the direction of choice and how the public responds to the use of music in politics, in which musicians are seen as tending to be politically affiliated with or
support certain candidates. Thus, a survey method was used to obtain these data. Through the approach of political
involvement theory and supported by interviews, it can be seen how musicians have strategies for creating political
songs. The results showed that some musicians chose to hide their names so that there would be no backlash
among their fans, while others felt afraid that their candidate would lose and affect their careers. This study provides a comprehensive picture of the condition of music in Indonesia that is in contact with the politics of the 2024
presidential election and becomes a kind of roadmap for the use of music in the world of politics in the future.
During the 2022 French presidential election, we collected daily Twitter messages on key topics posted by political candidates and their close networks. Using a data-driven approach, we analyze interactions among political parties, identifying central topics that shape the landscape of political debate. Moving beyond traditional correlation analyses, we apply a causal inference technique: Convergent Cross Mapping, to uncover directional influences among political communities, revealing how some parties are more likely to initiate changes in activity while others tend to respond. This approach allows us to distinguish true influence from mere correlation, highlighting asymmetric relationships and hidden dynamics within the social media political network. Our findings demonstrate how specific issues, such as health and foreign policy, act as catalysts for cross-party influence, particularly during critical election phases. These insights provide a novel framework for understanding political discourse dynamics and have practical implications for campaign strategists and media analysts seeking to monitor and respond to shifts in political influence in real time.
The rise of digital platforms has transformed political campaigning, introducing complex regulatory challenges. This paper presents a comprehensive taxonomy for analyzing political content in the EU's digital electoral landscape, aligning with the requirements set forth in new regulations, such as the Digital Services Act. Using a legal doctrinal methodology, we construct a detailed codebook that enables systematic content analysis across user-generated and political ad content to assess compliance with regulatory mandates.
Nina Smirnova, Muhammad Ahsan Shahid, Philipp Mayr
In this work, we present PoliCorp (https://demo-pollux.gesis.org/), a web portal designed to facilitate the search and analysis of political text corpora. PoliCorp provides researchers with access to rich textual data, enabling in-depth analysis of parliamentary discourse over time. The platform currently features a collection of transcripts from debates in the German parliament, spanning 76 years of proceedings. With the advanced search functionality, researchers can apply logical operations to combine or exclude search criteria, making it easier to filter through vast amounts of parliamentary debate data. The search can be customised by combining multiple fields and applying logical operators to uncover complex patterns and insights within the data. Additional data processing steps were performed to enable web-based search and incorporate extra features. A key feature that differentiates PoliCorp is its intuitive web-based interface that enables users to query processed political texts without requiring programming skills. The user-friendly platform allows for the creation of custom subcorpora via search parameters, which can be freely downloaded in JSON format for further analysis.
Myths—symbolically dense narratives in wide cultural circulation that resist critical scrutiny—are often thought to be counterproductive to political discourse, but they are also ubiquitous in contemporary culture and society. Just two years apart, Jürgen Habermas and Hans Blumenberg developed contrasting visions of how we ought to respond to the myths in our society. By reconstructing their disagreement, this paper uncovers the distinctive challenge of balancing a commitment to political emancipation with the opacity of myths to critical reason. I argue for an alternative approach to myths than those in the theoretical mainstream, taking Blumenberg’s relatively neglected position as a starting point. Blumenberg invites us to pay closer attention to the cognitive needs that necessitate the generation of myths while simultaneously reminding us of our own creative agency to reinvent them.
Pietro Bernardelle, Leon Fröhling, Stefano Civelli
et al.
The analysis of political biases in large language models (LLMs) has primarily examined these systems as single entities with fixed viewpoints. While various methods exist for measuring such biases, the impact of persona-based prompting on LLMs' political orientation remains unexplored. In this work we leverage PersonaHub, a collection of synthetic persona descriptions, to map the political distribution of persona-based prompted LLMs using the Political Compass Test (PCT). We then examine whether these initial compass distributions can be manipulated through explicit ideological prompting towards diametrically opposed political orientations: right-authoritarian and left-libertarian. Our experiments reveal that synthetic personas predominantly cluster in the left-libertarian quadrant, with models demonstrating varying degrees of responsiveness when prompted with explicit ideological descriptors. While all models demonstrate significant shifts towards right-authoritarian positions, they exhibit more limited shifts towards left-libertarian positions, suggesting an asymmetric response to ideological manipulation that may reflect inherent biases in model training.
Web search engines arguably form the most popular data-driven systems in contemporary society. They wield a considerable power by functioning as gatekeepers of the Web, with most user journeys on the Web beginning with them. Starting from the late 1990s, search engines have been dominated by the paradigm of link-based web search. In this paper, we critically analyze the political economy of the paradigm of link-based web search, drawing upon insights and methodologies from critical political economy. We draw several insights on how link-based web search has led to phenomena that favor capital through long-term structural changes on the Web, and how it has led to accentuating unpaid digital labor and ecologically unsustainable practices, among several others. We show how contemporary observations on the degrading quality of link-based web search can be traced back to the internal contradictions with the paradigm, and how such socio-technical phenomena may lead to a disutility of the link-based web search model. Our contribution is primarily on enhancing the understanding of the political economy of link-based web search, and laying bare the phenomena at work, and implicitly catalyze the search for alternative models.
Hubert Plisiecki, Paweł Lenartowicz, Maria Flakus
et al.
This paper investigates the presence of political bias in emotion inference models used for sentiment analysis (SA) in social science research. Machine learning models often reflect biases in their training data, impacting the validity of their outcomes. While previous research has highlighted gender and race biases, our study focuses on political bias - an underexplored yet pervasive issue that can skew the interpretation of text data across a wide array of studies. We conducted a bias audit on a Polish sentiment analysis model developed in our lab. By analyzing valence predictions for names and sentences involving Polish politicians, we uncovered systematic differences influenced by political affiliations. Our findings indicate that annotations by human raters propagate political biases into the model's predictions. To mitigate this, we pruned the training dataset of texts mentioning these politicians and observed a reduction in bias, though not its complete elimination. Given the significant implications of political bias in SA, our study emphasizes caution in employing these models for social science research. We recommend a critical examination of SA results and propose using lexicon-based systems as a more ideologically neutral alternative. This paper underscores the necessity for ongoing scrutiny and methodological adjustments to ensure the reliability and impartiality of the use of machine learning in academic and applied contexts.
The rising popularity of ChatGPT and other AI-powered large language models (LLMs) has led to increasing studies highlighting their susceptibility to mistakes and biases. However, most of these studies focus on models trained on English texts. Taking an innovative approach, this study investigates political biases in GPT's multilingual models. We posed the same question about high-profile political issues in the United States and China to GPT in both English and simplified Chinese, and our analysis of the bilingual responses revealed that GPT's bilingual models' political "knowledge" (content) and the political "attitude" (sentiment) are significantly more inconsistent on political issues in China. The simplified Chinese GPT models not only tended to provide pro-China information but also presented the least negative sentiment towards China's problems, whereas the English GPT was significantly more negative towards China. This disparity may stem from Chinese state censorship and US-China geopolitical tensions, which influence the training corpora of GPT bilingual models. Moreover, both Chinese and English models tended to be less critical towards the issues of "their own" represented by the language used, than the issues of "the other." This suggests that GPT multilingual models could potentially develop a "political identity" and an associated sentiment bias based on their training language. We discussed the implications of our findings for information transmission and communication in an increasingly divided world.
Developing machine learning models to characterize political polarization on online social media presents significant challenges. These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data. The common research practice typically examines the biased structure of online user communities for a given topic or qualitatively measuring the impacts of polarized topics on social media. However, there is limited work focusing on analyzing polarization at the ground-level, specifically in the social media posts themselves. Such existing analysis heavily relies on annotated data, which often requires laborious human labeling, offers labels only to specific problems, and lacks the ability to determine the near-future bias state of a social media conversations. Understanding the degree of political orientation conveyed in social media posts is crucial for quantifying the bias of online user communities and investigating the spread of polarized content. In this work, we first introduce two heuristic methods that leverage on news media bias and post content to label social media posts. Next, we compare the efficacy and quality of heuristically labeled dataset with a randomly sampled human-annotated dataset. Additionally, we demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts, employing both traditional supervised learning and few-shot learning setups. We conduct experiments using the proposed heuristic methods and machine learning approaches to predict the political orientation of posts collected from two social media forums with diverse political ideologies: Gab and Twitter.
The Narcissism of Minor Differences in the Context of Post-Imperial Macedonian Neighbouring
The conflicting relations among neighbouring nations in the Balkans may very accurately be explained by S. Freud’s theory of the Narcissism of Minor Differences. Related identities among nations and the bordering zones between countries have always been and continue to be a generator of racial, national, religious and cultural tensions. Whenever the discourse of identities is radicalized, cultural and political hegemony comes to life: identities are ranked according to worth; borders are changed according to national identity; methods of physical and metaphysical violence are used; shared places of memory are appropriated, and those not shared are negated. Perception is in crisis and, as a result, promotes a kind of conflictual mutual misrecognition. This text aims to demystify such installations of hegemony in the (North) Macedonian neighbouring region, and to articulate some principles of a post-hegemonistic paradigm.
Narcyzm małych różnic w kontekście postimperialnego sąsiedztwa Macedonii
Konfliktowe relacje między sąsiednimi narodami na Bałkanach można bardzo trafnie wyjaśnić teorią narcyzmu małych różnic Z. Freuda. Pokrewne tożsamości w obrębie tych narodów oraz stref przygranicznych między poszczególnymi krajami były i są generatorem napięć na tle rasowym, narodowym, religijnym i kulturowym. Ilekroć dyskurs o tożsamościach ulega radykalizacji, ożywa kulturowa i polityczna hegemonia: tożsamości są szeregowane według wartości; granice są zmieniane zgodnie z tożsamością narodową; stosowane są metody przemocy fizycznej i metafizycznej; współdzielone miejsca pamięci są zawłaszczane, a te, które nie są współdzielone, są negowane. Percepcja znajduje się w kryzysie i w rezultacie sprzyja rozwojowi wzajemnego niezrozumienia, które prowadzi do konfliktów. Celem niniejszego tekstu jest demistyfikacja takich działań o charakterze hegemonicznym w sąsiednim regionie (północnej) Macedonii oraz wyartykułowanie pewnych zasad paradygmatu posthegemonistycznego.
Ethnology. Social and cultural anthropology, Slavic languages. Baltic languages. Albanian languages
Xiao Ao, Danae Sánchez Villegas, Daniel Preoţiuc-Pietro
et al.
Parody is a figurative device used for mimicking entities for comedic or critical purposes. Parody is intentionally humorous and often involves sarcasm. This paper explores jointly modelling these figurative tropes with the goal of improving performance of political parody detection in tweets. To this end, we present a multi-encoder model that combines three parallel encoders to enrich parody-specific representations with humor and sarcasm information. Experiments on a publicly available data set of political parody tweets demonstrate that our approach outperforms previous state-of-the-art methods.
Using an unbalanced panel data covering 75 countries from 1991 to 2019, we explore how the political risk impacts on food reserve ratio. The empirical findings show that an increasing political risk negatively affect food reserve ratio, and same effects hold for both internal risk and external risk. Moreover, we find that the increasing external or internal risks both negatively affect production and exports, but external risk does not significantly impact on imports and it positively impacts on consumption, while internal risk negatively impacts on imports and consumption. The results suggest that most of governments have difficulty to raise subsequent food reserve ratio in face of an increasing political risk, no matter it is an internal risk or an external risk although the mechanisms behind the impacts are different.
Orestis Papakyriakopoulos, Christelle Tessono, Arvind Narayanan
et al.
Online platforms play an increasingly important role in shaping democracy by influencing the distribution of political information to the electorate. In recent years, political campaigns have spent heavily on the platforms' algorithmic tools to target voters with online advertising. While the public interest in understanding how platforms perform the task of shaping the political discourse has never been higher, the efforts of the major platforms to make the necessary disclosures to understand their practices falls woefully short. In this study, we collect and analyze a dataset containing over 800,000 ads and 2.5 million videos about the 2020 U.S. presidential election from Facebook, Google, and TikTok. We conduct the first large scale data analysis of public data to critically evaluate how these platforms amplified or moderated the distribution of political advertisements. We conclude with recommendations for how to improve the disclosures so that the public can hold the platforms and political advertisers accountable.
Polarization in America has reached a high point as markets are also becoming polarized. Existing research, however, focuses on specific market segments and products and has not evaluated this trend's full breadth. If such fault lines do spread into other segments that are not explicitly political, it would indicate the presence of lifestyle politics -- when ideas and behaviors not inherently political become politically aligned through their connections with explicitly political things. We study the pervasiveness of polarization and lifestyle politics over different product segments in a diverse market and test the extent to which consumer- and platform-level network effects and morality may explain lifestyle politics. Specifically, using graph and language data from Amazon (82.5M reviews of 9.5M products and product and category metadata from 1996-2014), we sample 234.6 million relations among 21.8 million market entities to find product categories that are most politically relevant, aligned, and polarized. We then extract moral values present in reviews' text and use these data and other reviewer-, product-, and category-level data to test whether individual- and platform- level network factors explain lifestyle politics better than products' implicit morality. We find pervasive lifestyle politics. Cultural products are 4 times more polarized than any other segment, products' political attributes have up to 3.7 times larger associations with lifestyle politics than author-level covariates, and morality has statistically significant but relatively small correlations with lifestyle politics. Examining lifestyle politics in these contexts helps us better understand the extent and root of partisan differences, why Americans may be so polarized, and how this polarization affects market systems.