<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2018-0003_s_007_w2aab3b7b3b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>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.</p></sec><sec id="j_jdis-2018-0003_s_008_w2aab3b7b3b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>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.</p></sec><sec id="j_jdis-2018-0003_s_009_w2aab3b7b3b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>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.</p></sec><sec id="j_jdis-2018-0003_s_010_w2aab3b7b3b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>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.</p></sec><sec id="j_jdis-2018-0003_s_011_w2aab3b7b3b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>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.</p></sec><sec id="j_jdis-2018-0003_s_012_w2aab3b7b3b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>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.</p></sec></abstract>

PurposeIn 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/approachWe 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.FindingsWe 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 limitationsIt 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 implicationsCompanies 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/valueThis 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.

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

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

PurposeIn this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users,...

Algorithm	Precision	Recall	F-score
Decision Tree	0.78	0.77	0.78
Support Vector Machine	0.78	0.76	0.77
Neural Networks	0.83	0.82	0.83
Naïve Bayes	0.81	0.82	0.81

	HasARealName (true)	NotHasARealName	Total
Protected	106,053 (55.37%)	217,541 (54.24%)	323,594 (54.61%)
Public	85,451 (44.62%)	183,464 (45.75%)	268,915 (45.38%)
Total	191,504 (100%)	401,005 (100%)	592,509 (100%)

	Female	Male	Undefined	Total
Protected Accounts	227,238 (55.34%)	288,235 (44.68%)	268,915 (45.38%)	784,388 (47.66%)
Public Accounts	183,358 (44.65%)	354,166 (55.13%)	323,594 (54.61%)	861,118 (52.33%)
Total	410,596 (100%)	642,401 (100%)	592,509 (100%)	1,645,506 (100%)

Results obtained by Khazaei et al. (2016a) classifiers				Results obtained by adding gender

Algorithm	Precision	Recall	F-Score	Precision	Recall	F-Score
Naïve Bayes	0.66	0.67	0.66	0.67	0.68	0.66
Regression	0.71	0.70	0.71	0.73	0.72	0.73
Logistic	0.69	0.70	0.69	0.69	0.71	0.69
J48	0.68	0.66	0.67	0.67	0.68	0.67
KNN	0.67	0.59	0.63	0.67	0.59	0.63

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

Article Category: Research Paper

Publié en ligne: 13 mars 2018

Pages: 40 - 53

Reçu: 19 avr. 2017

Accepté: 16 nov. 2017

DOI: https://doi.org/10.2478/jdis-2018-0003

Mots clés
Social media, Twitter, Gender recognition, Privacy preferences

© 2018 Walter de Gruyter GmbH, Berlin/Boston

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Evaluation of classification results.

Users whose genders cannot be inferred from the names and the privacy setting of their accounts.

User accounts’ privacy setting (protected vs public) and their gender inferred from their names and profile descriptions.

Comparison of our classification results with Khazaei et al. (2016a).

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

Article Category: Research Paper

Publié en ligne: 13 mars 2018

Pages: 40 - 53

Reçu: 19 avr. 2017

Accepté: 16 nov. 2017

DOI: https://doi.org/10.2478/jdis-2018-0003

Mots clésSocial media, Twitter, Gender recognition, Privacy preferences

© 2018 Walter de Gruyter GmbH, Berlin/Boston

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Evaluation of classification results.

Users whose genders cannot be inferred from the names and the privacy setting of their accounts.

User accounts’ privacy setting (protected vs public) and their gender inferred from their names and profile descriptions.

Comparison of our classification results with Khazaei et al. (2016a).

Mots clés
Social media, Twitter, Gender recognition, Privacy preferences