From Sentiment to Sensitivity: The Role of Emotions on Privacy Exposure in Twitter.

BSTRACTOnline Social Networks (OSNs) are a vital part of users’ daily lives. Users share content in OSNs increasingly more and in various emotional states. In this work, we explore the role of emotions on privacy exposure and we integrate it as an additional learning parameter in tweet sensitivity recognition. To this end, we first use BERT based classification techniques to recognize six basic emotions in tweets. Using our trained sentiment model, we further perform sentiment inference on a sensitivity dataset and integrate the sentiment in the BERT classification model to classify the tweets according to their sensitivity. We then compare the standard sensitivity recognition models’ results (with their tweets only) against the extended model that integrates the sentiment features in sensitivity recognition. We demonstrate that by including sentiment features in sensitivity analysis, our approach leads to about a 3% increase of f-1 score in contrast to using our base sensitivity classification, i.e., from 83.96% to 87.01% f-1 score. We further demonstrate a correlation between anger and disgust emotions with sensitive tweets, as well as, joy and surprise with non-sensitive tweets.