Challenge: This study examines the use of Catalan on Twitter in discourse related to the 2017 independence referendum.
Approach: They use code-switching to determine the role of Catalan in political discourse . they corroborate prior findings that pro-independence tweets are more likely to include the local language than anti-independent tweets .
Outcome: The proposed method corroborates previous findings that pro-independence tweets are more likely to include the local language than anti-independent tweets.

Similar Papers

Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus (2020.lrec-1)

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Challenge: stance detection is a method to determine the attitude of a text with respect to a specific topic or claim.
Approach: They propose a multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages.
Outcome: The proposed dataset shows that it is well balanced for multilingual and cross-lingual research.
Navigating the Political Compass: Evaluating Multilingual LLMs across Languages and Nationalities (2025.findings-acl)

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Challenge: Large Language Models (LLMs) are ubiquitous in today’s technological landscape, boasting a plethora of applications, and even endangering human jobs in complex and creative fields.
Approach: They evaluate the political bias of 15 multilingual LLMs using the Political Compass Test and assign a nationality to each model.
Outcome: The models on the 50 most populous countries and their official languages exhibit political bias.
Alignment, Acceptance, and Rejection of Group Identities in Online Political Discourse (N18-4)

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Challenge: linguistic alignment is a robust and robust form of communication accommodation, and has been detected in a variety of linguistic interactions, ranging from speed dates to the Supreme Court.
Approach: They propose a model to examine alignment in Twitter conversations across antagonistic groups.
Outcome: The proposed model adapts the WHAM alignment model to examine alignment in Twitter conversations across antagonistic groups.
Native Language Identification with User Generated Content (D18-1)

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Challenge: Using both linguistically-motivated features and the characteristics of the social media outlet, we obtain high accuracy on this challenging task.
Approach: They propose to use linguistically-motivated features and social media characteristics to obtain high accuracy on this task.
Outcome: The proposed method is highly accurate on a social media content where authors are highly-fluent nonnative speakers.
Semantics and Sentiment: Cross-lingual Variations in Emoji Use (2024.emnlp-main)

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Challenge: emojis have been used in social media for a decade but have been inconsistently used in contexts and in isolation.
Approach: They develop a corpus containing literal meanings for emojis defined by L1 speakers in three languages to assess their e-mail sentiments.
Outcome: The proposed method shows that emoji semantics differ across languages and how it interacts with sentiment in e-mails.
Predicting Foreign Language Usage from English-Only Social Media Posts (N18-2)

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Challenge: Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing.
Approach: They analyze 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English to build predictive models to infer non-English languages users speak exclusively from their tweets.
Outcome: The proposed models are based on a corpus of 6 million tweets produced by 27 thousand multilingual users speaking 12 other languages besides English . they show that content, style and syntax are the most predictive of non-English languages that users speak on Twitter.
A Case Study of Analysis of Construals in Language on Social Media Surrounding a Crisis Event (2021.acl-srw)

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Challenge: construal level theory (CLT) uses concreteness as covariate to analyze language around political import events.
Approach: They propose to include psycholinguistic measures of concreteness as covariates in topic models to analyze the language around an event of political import.
Outcome: The proposed model incorporates measures of concreteness as covariates to inform the analysis of language around the 2017 rally.
Do language models practice what they preach? Examining language ideologies about gendered language reform encoded in LLMs (2025.coling-main)

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Challenge: Language ideologies are evaluative ideas or beliefs about language, such as ideas about what is "correct", "natural" or "articulate".
Approach: They use gender-neutral variants more often when more explicit metalinguistic context is provided.
Outcome: The findings show that language ideologies in LLMs can vary, which may be unexpected to users.
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings (N19-1)

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Challenge: a new framework for studying political polarization in social media is needed to understand how group divisions manifest in language.
Approach: They propose to cluster tweet embeddings to uncover four dimensions of political polarization in social media . their results apply existing lexical methods to analyze 4.4M tweets on 21 mass shootings .
Outcome: The proposed framework generates more cohesive topics than traditional models.
Does Context Matter? A Prosodic Comparison of English and Spanish in Monolingual and Multilingual Discourse Settings (2025.emnlp-main)

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Challenge: a large number of studies on prosody in languages have focused on monolingual discourse contexts . a recent study focused on the prosodic features of monolingual speech in multilingual contexts.
Approach: They compare prosody of monolingual English and Spanish in monolingual and multilingual settings . they find that monolingual speech produced in a monolingual context is prosodically different from that produced in multilingual context .
Outcome: The proposed study is the first to incorporate multilingual discourse contexts into the study of native-level monolingual prosody.

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