Predicting political preference of Twitter users

By Aibek Makazhanov, Davood Rafiei. 2013

153 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Natural Language Processing

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Citations in the corpus

Ethical and social risks of harm from Language Models (2021, background): “Language utterances (e.g. tweets) are already being analysed to predict private information such as political orientation (Makazhanov et al., 2014; Preoţiuc-Pietro et al., 2017), age (Morgan-Lopez et al., 2017; Nguyen et al., 2013), and health data such as addiction relapses (Golbeck, 2018).”
Taxonomy of Risks posed by Language Models (2022, background): “Language utterances (e.g. Tweets) are already being analysed to predict private information such as political orientation [121, 144], age [131, 135], and health data such as addiction relapses [63].”
Predictive Analysis on Twitter: Techniques and Applications (2018, methodology): “Researchers [39, 89, 90] used Twitter data to predict political opinions of users based on linguistic characteristics (e.”
Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017, background): “Instead, many previous studies have tried to infer the bias of a tweet’s source (the user who posted it), by modeling the language usage of the users from different political polarities [23, 32] or by leveraging the social links that a user has with other Twitter users with known biases [6,13].”
Bias in Wikipedia (2017, background): “Makazhanov et al. [16] show that language features can be used as a strong indicator for political leaning.”
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models (2023, background): “Understanding Social Bias of LMs Studies have been conducted to measure political biases and predict the ideology of individual users (Colleoni et al., 2014; Makazhanov and Rafiei, 2013; Preoţiuc-Pietro et al., 2017), news articles (Li and Goldwasser, 2019; Feng et al.”