War of Words: The Competitive Dynamics of Legislative Processes

Kristof, Victor; Grossglauser, Matthias; Thiran, Patrick

doi:10.1145/3366423.3380041

Cited by 4 publications

(7 citation statements)

References 12 publications

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“…The literature is also interested in the phenomenon specific to the Wikipedia platform. "Edit wars" is, for instance, one of the topics to which researchers pay attention that refers to a phenomenon in which users repeatedly edit the content of a particular ar-ticle, often one on controversial topics (Borra et al 2015;Kristof, Grossglauser, and Thiran 2021;Jaidka et al 2021).…”

Section: Related Workmentioning

confidence: 99%

Throw Your Hat in the Ring (of Wikipedia): Exploring Urban-Rural Disparities in Local Politicians' Information Supply

Matsui,

Miyazaki,

Murayama

2024

ICWSM

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In this era of digital politics, understanding the factors that influence the supply of political information is important. This study investigates the relationship between socio-economic status and the political information supplied on Wikipedia. To this end, it employs a dataset of politicians who ran for local elections in Japan over approximately 20 years and discovers that the creation and revisions of local politicians' pages are associated with socio-economic factors such as the employment ratio by industry and age distribution. We find that the majority of the suppliers of politicians' information are unregistered and primarily interested in politicians' pages compared to registered users. Additional analysis reveals that users who supply information about politicians before and after an election are more active on Wikipedia than the average user. The findings presented imply that the information supply on Wikipedia, which relies on voluntary contributions, may reflect regional socio-economic disparities.

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Section: Related Workmentioning

confidence: 99%

Throw Your Hat in the Ring (of Wikipedia): Exploring Urban-Rural Disparities in Local Politicians' Information Supply

Matsui,

Miyazaki,

Murayama

2024

ICWSM

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“…We also apply our graph-level polarization measure to characterize real-world signed graphs. In addition to Congress, we consider five other networks, with their statistics summarized in Table 1: • WoW-EP8 [27]: Interaction network of editors in the eighth legislature of the European Parliament. Link signs indicate whether they collaborate or compete with each other.…”

Section: Graph-level Polarizationmentioning

confidence: 99%

Pole

Huang

Silva

Singh

2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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From the 2016 U.S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society. Recent advances in machine learning for signed networks hold the promise to guide small interventions with the goal of reducing polarization in social media. However, existing models are especially ineffective in predicting conflicts (or negative links) among users. This is due to a strong correlation between link signs and the network structure, where negative links between polarized communities are too sparse to be predicted even by state-of-the-art approaches. To address this problem, we first design a partition-agnostic polarization measure for signed graphs based on the signed random-walk and show that many real-world graphs are highly polarized. Then, we propose POLE (POLarized Embedding for signed networks), a signed embedding method for polarized graphs that captures both topological and signed similarities jointly via signed autocovariance. Through extensive experiments, we show that POLE significantly outperforms state-of-the-art methods in signed link prediction, particularly for negative links with gains of up to one order of magnitude. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Information systems → Social networks.

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“…However, such data contains other markers. For instance, a study on thirty years of election debates from the US politician shows that linguistic features can predict the media coverage [18], [19] or the prediction of legislative discussions to be adapted into law [20]. Figure 3 describes the features related to political debates.…”

Section: B Descriptive Frameworkmentioning

confidence: 99%

A Survey on Computational Politics

et al. 2020

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Computational Politics is the study of computational methods to analyze and moderate users' behaviors related to political activities such as election campaign persuasion, political affiliation, and opinion mining. With the rapid development and ease of access to the Internet, Information Communication Technologies (ICT) have given rise to massive numbers of users joining online communities and the digitization of political practices such as debates. These communities and digitized data contain both explicit and latent information about users and their behaviors related to politics and social movements. For researchers, it is essential to utilize data from these sources to develop and design systems that not only provide solutions to computational politics but also help other businesses, such as marketers, to increase users' participation and interactions. In this survey, we attempt to categorize main areas in computational politics and summarize the prominent studies in one place to better understand computational politics across different and multidimensional platforms. e.g., online social networks, online forums, and political debates. We then conclude this study by highlighting future research directions, opportunities, and challenges.

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War of Words: The Competitive Dynamics of Legislative Processes

Cited by 4 publications

References 12 publications

Throw Your Hat in the Ring (of Wikipedia): Exploring Urban-Rural Disparities in Local Politicians' Information Supply

Throw Your Hat in the Ring (of Wikipedia): Exploring Urban-Rural Disparities in Local Politicians' Information Supply

Pole

A Survey on Computational Politics

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