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Accordingly, there has been much research on political polarization, including many recent efforts that take a computational perspective. By detecting political biases in a text document, one can attempt to discern and describe its polarity. Intuitively, the named entities (i.e., the nouns and the phrases that act as nouns) and hashtags in text often carry information about political views. For example, people who use the term \u201cpro-choice\u201d are likely to be liberal and people who use the term \u201cpro-life\u201d are likely to be conservative. In this paper, we seek to reveal political polarities in social-media text data and to quantify these polarities by explicitly assigning a polarity score to entities and hashtags. Although this idea is straightforward, it is difficult to perform such inference in a trustworthy quantitative way. Key challenges include the small number of known labels, the continuous spectrum of political views, and the preservation of both a polarity score and a polarity-neutral semantic meaning in an embedding vector of words. To attempt to overcome these challenges, we propose the <jats:bold>P<\/jats:bold>olarity-aware <jats:bold>E<\/jats:bold>mbedding <jats:bold>M<\/jats:bold>ulti-task learning (<jats:bold>PEM<\/jats:bold>) model. This model consists of (1) a self-supervised context-preservation task, (2) an attention-based tweet-level polarity-inference task, and (3) an adversarial learning task that promotes independence between an embedding\u2019s polarity component and its semantic component. Our experimental results demonstrate that our <jats:bold>PEM<\/jats:bold> model can successfully learn polarity-aware embeddings that perform well at tweet-level and account-level classification tasks. We examine a variety of applications\u2014including a study of spatial and temporal distributions of polarities and a comparison between tweets from Twitter and posts from Parler\u2014and we thereby demonstrate the effectiveness of our <jats:bold>PEM<\/jats:bold> model. We also discuss important limitations of our work and encourage caution when applying the <jats:bold>PEM<\/jats:bold> model to real-world scenarios.<\/jats:p>","DOI":"10.1140\/epjds\/s13688-023-00386-6","type":"journal-article","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T14:02:36Z","timestamp":1686232956000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Detecting political biases of named entities and hashtags on Twitter"],"prefix":"10.1140","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8583-4789","authenticated-orcid":false,"given":"Zhiping","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yining","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen Hong","family":"Lam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5166-0717","authenticated-orcid":false,"given":"Mason A.","family":"Porter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhou","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"386_CR1","doi-asserted-by":"publisher","DOI":"10.7208\/chicago\/9780226473673.001.0001","volume-title":"The partisan sort: How liberals became Democrats and conservatives became Republicans","author":"M Levendusky","year":"2009","unstructured":"Levendusky M (2009) The partisan sort: How liberals became Democrats and conservatives became Republicans. 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