Gender differences in the perception of security of mobile phones

Sieger, Hanul; Möller, Sebastian

doi:10.1145/2371664.2371685

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…Our work builds on the previous studies that demonstrate the correlation between a person's gender and the level of his/her privacy concern. Many studies indicate that women tend to be more concerned about online privacy and security issues than men (Adam, 2000;Sieger & Moller, 2012). But when the sites are related to shopping or fashion women-related online behaviors are contradictory with their alleged high privacy concerns (Katell, Mishra, & Scaff, 2016;Nazir et al, 2012).…”

Section: Research Papermentioning

confidence: 99%

“…(Adam, 2000). It is also suggested that there are differences between men and women's ethical decision-making in relation to information and computing technologies (ICT's) (Sieger & Moller, 2012). Adam (2000) surveyed a sample of undergraduate and graduate business students and found that women have different responses to men regarding computer privacy.…”

Section: Privacy and Gendermentioning

confidence: 99%

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

Espinosa

Xiao

2018

Journal of Data and Information Science

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Purpose In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements. Design/methodology/approach We worked with two different data sets to examine whether Twitter 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 the inferred 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’s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user’s privacy preference. Research limitations It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A major limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, 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 business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users’ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users’ provided first names and their profile descriptions, can be used to infer the users’ privacy preference. Originality/value This study explored a new way of inferring Twitter user’s gender, that is, to recognize the user’s gender based on the provided first name and the user’s profile description. The potential of this information for predicting the user’s privacy preference is explored.

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Section: Research Papermentioning

confidence: 99%