Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866914951147290624 |
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| author | Madani, Navid Bandyopadhyay, Rabiraj Swire-Thompson, Briony Yoder, Michael Miller Joseph, Kenneth |
| author_facet | Madani, Navid Bandyopadhyay, Rabiraj Swire-Thompson, Briony Yoder, Michael Miller Joseph, Kenneth |
| contents | Social media platforms provide users with a profile description field, commonly known as a ``bio," where they can present themselves to the world. A growing literature shows that text in these bios can improve our understanding of online self-presentation and behavior, but existing work relies exclusively on keyword-based approaches to do so. We here propose and evaluate a suite of \hl{simple, effective, and theoretically motivated} approaches to embed bios in spaces that capture salient dimensions of social meaning, such as age and partisanship. We \hl{evaluate our methods on four tasks, showing that the strongest one out-performs several practical baselines.} We then show the utility of our method in helping understand associations between self-presentation and the sharing of URLs from low-quality news sites on Twitter\hl{, with a particular focus on explore the interactions between age and partisanship, and exploring the effects of self-presentations of religiosity}. Our work provides new tools to help computational social scientists make use of information in bios, and provides new insights into how misinformation sharing may be perceived on Twitter. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_09548 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing Madani, Navid Bandyopadhyay, Rabiraj Swire-Thompson, Briony Yoder, Michael Miller Joseph, Kenneth Computation and Language Social media platforms provide users with a profile description field, commonly known as a ``bio," where they can present themselves to the world. A growing literature shows that text in these bios can improve our understanding of online self-presentation and behavior, but existing work relies exclusively on keyword-based approaches to do so. We here propose and evaluate a suite of \hl{simple, effective, and theoretically motivated} approaches to embed bios in spaces that capture salient dimensions of social meaning, such as age and partisanship. We \hl{evaluate our methods on four tasks, showing that the strongest one out-performs several practical baselines.} We then show the utility of our method in helping understand associations between self-presentation and the sharing of URLs from low-quality news sites on Twitter\hl{, with a particular focus on explore the interactions between age and partisanship, and exploring the effects of self-presentations of religiosity}. Our work provides new tools to help computational social scientists make use of information in bios, and provides new insights into how misinformation sharing may be perceived on Twitter. |
| title | Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2305.09548 |