# Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing

arXiv: https://arxiv.org/abs/2305.09548 · HTML: https://mlsanity.com/paper/2305.09548/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 91 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [Measuring Bias in Contextualized Word Representations](https://mlsanity.com/paper/1906.07337/) (2019)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Demographic Dialectal Variation in Social Media: A Case Study of African-American English](https://mlsanity.com/paper/1608.08868/) (2016)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Understanding Undesirable Word Embedding Associations](https://mlsanity.com/paper/1908.06361/) (2019)
- [A Framework for the Computational Linguistic Analysis of Dehumanization](https://mlsanity.com/paper/2003.03014/) (2020)
- [Entity-Centric Contextual Affective Analysis](https://mlsanity.com/paper/1906.01762/) (2019)
- [When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?](https://mlsanity.com/paper/2004.12043/) (2020)
- [Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories](https://mlsanity.com/paper/1904.04164/) (2019)
- [A Method to Analyze Multiple Social Identities in Twitter Bios](https://mlsanity.com/paper/2107.14120/) (2021)
- [Friend or Foe: A Review and Synthesis of Computational Models of the Identity Labeling Problem](https://mlsanity.com/paper/2105.04462/) (2021)
- [Finding Qs: Profiling QAnon Supporters on Parler](https://mlsanity.com/paper/2205.08834/) (2022)

## Built on by (most cited first, 1 of 1)

- [AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters](https://mlsanity.com/paper/2401.06408/) (2024)

## Abstract (arXiv)

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 hlsimple, effective, and theoretically motivated approaches to embed bios in spaces that capture salient dimensions of social meaning, such as age and partisanship. We hlevaluate 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 Twitterhl, 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.