Predicting the Topical Stance and Political Leaning of Media using Tweets (2020.acl-main)
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| Challenge: | Existing methods for determining stances of media outlets and influential people are expensive. |
| Approach: | They propose a method that uses unsupervised learning to ascertain the stance of Twitter users with respect to a polarizing topic by leveraging their retweet behavior. |
| Outcome: | The proposed method achieves 82.6% accuracy compared to gold labels from the Media Bias/Fact Check website . |
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| Challenge: | Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. |
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| Challenge: | stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target. |
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On the Reliability and Validity of Detecting Approval of Political Actors in Tweets (2020.emnlp-main)
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| Challenge: | Social media sites have the potential to complement surveys that measure political opinions and, more specifically, political actors’ approval. |
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A Few Topical Tweets are Enough for Effective User Stance Detection (2021.eacl-main)
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| Challenge: | Recent work that employs unsupervised classification has shown that user stance detection is highly accurate on vocal Twitter users, but fails for less vocal users, who may have only authored a few tweets about a target. |
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Toxicity, Morality, and Speech Act Guided Stance Detection (2023.findings-emnlp)
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| Challenge: | Existing studies that focus on stance detection ignore the speech act, toxic, and moral features of tweets or lack an efficient architecture to detect the attitudes across targets. |
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Stance Detection in COVID-19 Tweets (2021.acl-long)
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| Challenge: | a global pandemic of COVID-19 has forced major changes in our daily lives . a new stance detection dataset is being used to track the stances of Twitter users . |
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Political Ideology and Polarization: A Multi-dimensional Approach (2022.naacl-main)
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| Challenge: | Recent research has made great strides towards understanding the ideological bias (i.e., stance) of news media along the left-right spectrum. |
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Classification without (Proper) Representation: Political Heterogeneity in Social Media and Its Implications for Classification and Behavioral Analysis (2022.findings-acl)
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| Challenge: | Prior work has shown that partisan leanings can be inferred from a diverse set of behavioral characteristics such as text, social networks, and even community participation. |
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Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media (2022.naacl-main)
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| Challenge: | Existing approaches to detect vaccine attitudes on social media require abundant annotations and pre-defined aspect categories. |
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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 . |
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