Facebook versus Twitter: Differences in Self-Disclosure and Trait Prediction

Vol. 12 No. 1 (2018): Twelfth International AAAI Conference on Web and Social Media

Volume

Vol. 12 No. 1 (2018): Twelfth International AAAI Conference on Web and Social Media

Full Papers

This study compares self-disclosure on Facebook and Twitter through the lens of demographic and psychological traits. Predictive evaluation reveals that language models trained on Facebook posts are more accurate at predicting age, gender, stress, and empathy than those trained on Twitter posts. Qualitative analyses of the underlying linguistic and demographic differences reveal that users are significantly more likely to disclose information about their family, personal concerns, and emotions and provide a more `honest' self-representation on Facebook. On the other hand, the same users significantly preferred to disclose their needs, drives, and ambitions on Twitter. The higher predictive performance of Facebook is also partly due to the greater volume of language on Facebook than Twitter -- Facebook and Twitter are equally good at predicting user traits when the same-sized language samples are used to train language models. We explore the implications of these differences in cross-platform user trait prediction.

10.1609/icwsm.v12i1.15026