Twitter Users’ Privacy Concerns: What do Their Accounts’ First Names Tell Us?

摘要

Purpose: In this paper, we describe how gender recognition on Twitter can be used as anintelligent business tool to determine the privacy concerns among users, and ultimately offera more personalized service for customers who are more likely to respond positively totargeted advertisements.Design/methodology/approach: We worked with two different data sets to examine whetherTwitter users’ gender, inferred from the first name of the account and the profile description,correlates with the privacy setting of the account. We also used a set of features including theinferred gender of Twitter users to develop classifiers that predict user privacy settings.Findings: We found that the inferred gender of Twitter users correlates with the account’sprivacy setting. Specifically, females tend to be more privacy concerned than males. Userswhose gender cannot be inferred from their provided first names tend to be more privacyconcerned. In addition, our classification performance suggests that inferred gender can beused as an indicator of the user’s privacy preference.Research limitations: It is known that not all twitter accounts are real user accounts, andsocial bots tweet as well. A major limitation of our study is the lack of consideration of socialbots in the data. In our study, this implies that at least some percentage of the undefinedaccounts, that is, accounts that had names non-existent in the name dictionary, are social bots.It will be interesting to explore the privacy setting of social bots in the Twitter space.Practical implications: Companies are investing large amounts of money in businessintelligence tools that allow them to know the preferences of their consumers. Due to the largenumber of consumers around the world, it is very difficult for companies to have directcommunication with each customer to anticipate market changes. For this reason, the socialnetwork Twitter has gained relevance as one ideal tool for information extraction. On theother hand, users’ privacy preference needs to be considered when companies considerleveraging their publicly available data. This paper suggests that gender recognition of Twitterusers, based on Twitter users’ provided first names and their profile descriptions, can be usedto infer the users’ privacy preference.