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Linguistic Bias in Crowdsourced Biographies A Cross-lingual Examination ...
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Linguistic Bias in Crowdsourced Biographies A Cross-lingual Examination ...
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Social Cues, Social Biases: Stereotypes in Annotations on People Images ...
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Social Cues, Social Biases: Stereotypes in Annotations on People Images ...
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Social Cues, Social Biases: Stereotypes in Annotations on People Images
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In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing; Vol. 6 No. 1 (2018): Sixth AAAI Conference on Human Computation and Crowdsourcing (2018)
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The Impact of News Values and Linguistic Style on the Popularity of Headlines On Twitter and Facebook
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 11 No. 1 (2017): Eleventh International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2017)
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Similar Gaps, Different Origins? Women Readers and Editors at Greek Wikipedia
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 10 No. 1 (2016): Tenth International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2016)
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Social and Linguistic Behavior and its Correlation to Trait Empathy
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Show Me You Care: Trait Empathy, Linguistic Style and Mimicry on Facebook
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Linguistic Bias in Collaboratively Produced Biographies: Crowdsourcing Social Stereotypes?
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 9 No. 1 (2015): Ninth International AAAI Conference on Web and Social Media ; 2334-0770 ; 2162-3449 (2015)
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Write Like I Write: Herding in the Language of Online Reviews
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In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 8 No. 1 (2014): Eighth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2014)
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Abstract:
Our behaviors often converge with those of others, and language within social media is no exception. We consider reviews of tourist attractions at TripAdvisor (TA), the world's largest resource for travel information. Unlike social networking sites, TA review forums do not facilitate direct interaction between participants. Nonetheless, theory suggests that language is guided by writers' conception of their audience, and that their style can shift in response. We implement a model of herding as a local transmission process, exploring the hypothesis that a reviewer is influenced by how preceding reviews manifest a given stylistic feature (e.g., pronouns, paralinguistic devices). We find that reviewers are more likely to use unusual features when such characteristics appear in their local context. The extent to which reviewers are influenced by context is correlated to attributes shared in their profiles, as well as their sentiment toward the attraction reviewed. Our results suggest that language can be influenced by others, even in an asynchronous environment with little to no interpersonal interaction. In other words, our behaviors may be susceptible to manipulation in social media; it may not always be the case that we write like ourselves.
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Keyword:
computational model; herding; linguistic style
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URL: https://ojs.aaai.org/index.php/ICWSM/article/view/14511
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MEAD - A Platform for Multidocument Multilingual Text Summarization
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MEAD - A Platform for Multidocument Multilingual Text Summarization ...
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