Papers by Tanise Ceron

4 papers
Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers (2023.emnlp-main)

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Challenge: Scaling analysis is a technique that assigns a political actor a score on a predefined scale based on 'typically long' text.
Approach: They propose to use label aggregation and long-input-Transformer-based models to automatically scale political-party manifestos.
Outcome: The proposed models can scale political platforms on a predefined scale based on 'left-right' scales and work robustly across domains and languages.
Do Political Opinions Transfer Between Western Languages? An Analysis of Unaligned and Aligned Multilingual LLMs (2026.eacl-long)

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Challenge: Public opinion surveys show cross-cultural differences in political opinions between socio-cultural contexts.
Approach: They analyze whether opinions transfer between languages or whether there are separate opinions for each language in multilingual large language models of various sizes across five Western languages.
Outcome: The political alignment shifts opinions almost uniformly across all five languages.
Toeing the Party Line: Election Manifestos as a Key to Understand Political Discourse on Twitter (2024.findings-emnlp)

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Challenge: Recent work on political positioning on Twitter has tended to focus on manifestos rather than social media since it is ambiguous and dependent on social context.
Approach: They propose to use hashtags as a signal to fine-tune text representations for politicians' tweets using a hashtag-based method to predict pairwise positional similarities between parties from the manifesto case to the Twitter case.
Outcome: The proposed method matches politicians' statements to official lines of the parties' tweets, even when only small subsets from shorter time periods are available.
Additive manifesto decomposition: A policy domain aware method for understanding party positioning (2023.findings-acl)

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Challenge: Existing methods for estimating policy domain aware party similarity are limited to global domains.
Approach: They propose a workflow for estimating policy domain aware party similarity by aggregating policy domains into a single figure . they use a set of tools to extract party positions on major policy axes via multidimensional scaling.
Outcome: The proposed method yields high correlation when predicting party similarity at a global level and provides accurate party-specific positions even with automatically labelled policy domains.

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