This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Adam, A. (2000). Gender and computer ethics. ACM SIGCAS Computers and Society, 30(4), 17–24.10.1145/572260.572265AdamA.2000Gender and computer ethics3041724Open DOISearch in Google Scholar
Ale, F.L. (2015). What is market segmentation? (Merca 2.0) Retrieved from http://www.merca20.com/que-es-la-segmentacion-de-mercados/.AleF.L.2015What is market segmentation?Retrieved fromhttp://www.merca20.com/que-es-la-segmentacion-de-mercados/Search in Google Scholar
Argamon, S., Koppel, M., Fine, J., & Shimoni, A.R. (2006). Gender, genre, and writing style in formal written texts. Text-Interdisciplinary Journal for the Study of Discourse, 23(3), 321–346.ArgamonS.KoppelM.FineJ.ShimoniA.R.2006Gender, genre, and writing style in formal written texts23332134610.1515/text.2003.014Search in Google Scholar
Burger, J.D., Henderson, J., Kim, G., & Zarrella, G. (2011). Discriminating gender on Twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 1301–1309). Stroudsburg, PA: Association for Computational Linguistics.BurgerJ.D.HendersonJ.KimG.ZarrellaG.2011Discriminating gender on Twitter13011309Stroudsburg, PAAssociation for Computational LinguisticsSearch in Google Scholar
Campbell, M. (2016). About our site. (Behind the Name: the Etymology and History of First Names). Retrieved from http://www.behindthename.com.CampbellM.2016About our siteRetrieved fromhttp://www.behindthename.comSearch in Google Scholar
Chu, Z., Gianvecchio, S., Wang, H., & Jajodia, S. (2010). Who is tweeting on Twitter: Human, bot, or cyborg? In Proceedings of the 26th Annual Computer Security Applications Conference (pp. 21–30). New York: ACM.ChuZ.GianvecchioS.WangH.JajodiaS.2010Who is tweeting on Twitter: Human, bot, or cyborg?2130New YorkACM10.1145/1920261.1920265Search in Google Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates.CohenJ.19882ndHillsdale, NJLawrence Earlbaum AssociatesSearch in Google Scholar
Entrepreneur Magazine. (2012). How to know your market. Retrieved from https://www.entrepreneur.com/article/264931.Entrepreneur Magazine2012Retrieved fromhttps://www.entrepreneur.com/article/264931Search in Google Scholar
Fernandez, D., Moctezuma, D., & Sordia, O. (2016). Features combination for gender recognition on Twitter Users. IEEE International Autumn Meeting on Power, Electronics and Computing, 17(17), 400–408.FernandezD.MoctezumaD.SordiaO.2016Features combination for gender recognition on Twitter Users171740040810.1109/ROPEC.2016.7830623Search in Google Scholar
Gaucher, D., Friesen, J., & Kay, A.C. (2011). Evidence that gendered wording in job advertisements exists and sustains gender inequality. Journal of Personality and Social Psychology, 101(1), 109–128.10.1037/a002253021381851GaucherD.FriesenJ.KayA.C.2011Evidence that gendered wording in job advertisements exists and sustains gender inequality101110912821381851Open DOISearch in Google Scholar
Gonzalez, F. (2015). New Twitter tool promising better targeting. (Merka 2.0). Retrieved from http://www.merca20.com/nueva-herramienta-de-twitter-que-promete-una-mejor-segmentacion-del-target/.GonzalezF.2015Merka 2.0. Retrieved fromhttp://www.merca20.com/nueva-herramienta-de-twitter-que-promete-una-mejor-segmentacion-del-target/Search in Google Scholar
Greenfield, C. (2012). Don’t be creepy: Using robust user data for ad targeting while respecting privacy. (Target Marketing). Retrieved from http://www.targetmarketingmag.com/post/don-t-creeper-utilizing-robust-user-data-ad-targeting-while-respecting-privacy/all/.GreenfieldC.2012Don’t be creepy: Using robust user data for ad targeting while respecting privacyRetrieved fromhttp://www.targetmarketingmag.com/post/don-t-creeper-utilizing-robust-user-data-ad-targeting-while-respecting-privacy/all/Search in Google Scholar
Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social networks. In Proceedings of the 2005 ACM Workshop on Privacy in the Electronic Society (pp. 71–80). Alexandria: ACM.GrossR.AcquistiA.2005Information revelation and privacy in online social networks7180AlexandriaACM10.1145/1102199.1102214Search in Google Scholar
Herdağdelen, A. (2013). Twitter n-gram corpus with demographic metadata. Language Resources and Evaluation, 47(4), 1127–1147.10.1007/s10579-013-9227-2HerdağdelenA.2013Twitter n-gram corpus with demographic metadata47411271147Open DOISearch in Google Scholar
Howland, S. (2014). Anticipate trends ensure business success. (Chiefexecutive.net) Retrieved from http://chiefexecutive.net/anticipate-trends-ensure-business-success/3/.HowlandS.2014Anticipate trends ensure business successRetrieved fromhttp://chiefexecutive.net/anticipate-trends-ensure-business-success/3/Search in Google Scholar
Irani, D., Webb, S., Li, K., & Pu, C. (2009). Large Online Social Footprints–An Emerging Threat. In CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering (pp. 271–276). Washington, D.C.: IEEE Computer Society.IraniD.WebbS.LiK.PuC.2009Large Online Social Footprints–An Emerging Threat271276Washington, D.C.IEEE Computer Society10.1109/CSE.2009.459Search in Google Scholar
Katell, M.A., Mishra, S.R., & Scaff, L. (2016). A fair exchange: Exploring how online privacy is valued. In the 49th Hawaii International Conference on System Sciences (HICSS) (2016) (pp: 1881–1890). Washington, D.C.: IEEE Computer Society.KatellM.A.MishraS.R.ScaffL.2016A fair exchange: Exploring how online privacy is valued18811890Washington, D.C.IEEE Computer Society10.1109/HICSS.2016.239Search in Google Scholar
Khazaei, T., Xiao, L., Mercer, R.E., & Khan, A. (2016a). Privacy Preference Inference via Collaborative Filtering. In Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM), 611–614.KhazaeiT.XiaoL.MercerR.E.KhanA.2016aPrivacy Preference Inference via Collaborative Filtering61161410.1609/icwsm.v10i1.14770Search in Google Scholar
Khazaei, T., Xiao, L., Mercer, R.E., & Khan, A. (2016b). Privacy behaviour and profile configuration in Twitter. In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 575–580). Montreal.KhazaeiT.XiaoL.MercerR.E.KhanA.2016bPrivacy behaviour and profile configuration in Twitter575580Montreal10.1145/2872518.2890088Search in Google Scholar
Krempeaux, C.I. (2013). Predicting gender on Twitter. (Charles Iliya Krempeaux Personal Site) Retrieved from http://changelog.ca/log/2013/03/03/twitter_gender.KrempeauxC.I.2013Predicting gender on TwitterRetrieved fromhttp://changelog.ca/log/2013/03/03/twitter_genderSearch in Google Scholar
Kwasny, M., Caine, K., Rogers, W.A., & Fisk, A.D. (2008). Privacy and technology: Folk definitions and perspectives. In CHI '08 Extended Abstracts on Human Factors in Computing Systems (pp. 3291–3296). New York: ACM.KwasnyM.CaineK.RogersW.A.FiskA.D.2008Privacy and technology: Folk definitions and perspectives32913296New YorkACM10.1145/1358628.1358846564787729057397Search in Google Scholar
Liu, W., & Ruths, D. (2013). What’s in a name? Using first names as features for gender inference in Twitter. In Analyzing Microtext: Papers from the 2013 AAAI Spring Symposium (pp. 10–16). Palo Alto, CA: AAAI Press.LiuW.RuthsD.2013What’s in a name? Using first names as features for gender inference in Twitter1016Palo Alto, CAAAAI PressSearch in Google Scholar
Lopez, N. (2014). Twitter advertisers can now target ads based on the apps a user has installed. (The next web). Retrieved from http://thenextweb.com/insider/2014/12/09/twitter-advertisers-can-now-monitor-user-behavior-mobile-apps-ad-targeting/.LopezN.2014Twitter advertisers can now target ads based on the apps a user has installedRetrieved fromhttp://thenextweb.com/insider/2014/12/09/twitter-advertisers-can-now-monitor-user-behavior-mobile-apps-ad-targeting/Search in Google Scholar
Morey, T., Forbath, T., & Schoop, A. (2015). Customer data: Designing for transparency and trust. (Harvard Business Review). Retrieved from https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust.MoreyT.ForbathT.SchoopA.2015Customer data: Designing for transparency and trustRetrieved fromhttps://hbr.org/2015/05/customer-data-designing-for-transparency-and-trustSearch in Google Scholar
Nazir, S., Tayyab, A., Sajid, A., Rashid, H.u., & Javed, I. (2012). How online shopping is affecting consumers buying behavior in Pakistan? IJCSI International Journal of Computer Science Issues, 9(3), 486–495.NazirS.TayyabA.SajidA.RashidH.u.JavedI.2012How online shopping is affecting consumers buying behavior in Pakistan?93486495Search in Google Scholar
Parker, R.B. (1974). A definition of privacy. Rutgers Law Review, 27(1), 275–296.ParkerR.B.1974A definition of privacy27127529610.4324/9781315246024-5Search in Google Scholar
Riquelme, I.P., & Román, S. (2014). Is the influence of privacy and security on online trust the same for all type of consumers? Electronic Markets, 24(2), 135–149.10.1007/s12525-013-0145-3RiquelmeI.P.RománS.2014Is the influence of privacy and security on online trust the same for all type of consumers?242135149Open DOISearch in Google Scholar
Sieger, H., & Moller, S. (2012). Gender differences in the perception of security of mobile phones. In Proceedings of the 14th International Conference on Human-computer Interaction with Mobile Devices and Services Companion (pp. 107–112). New York: ACM.SiegerH.MollerS.2012Gender differences in the perception of security of mobile phones107112New YorkACM10.1145/2371664.2371685Search in Google Scholar
Statista. (2016). Global social networks ranked by number of users. Retrieved from https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/.Statista2016Retrieved fromhttps://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/Search in Google Scholar
Twitter. (2017). Users. Retrieved from https://dev.twitter.com/overview/api/users.Twitter2017Retrieved fromhttps://dev.twitter.com/overview/api/usersSearch in Google Scholar
van Aswegen, A. (2015). Women vs. men—Gender differences in purchase decision making (Guided Selling) Retrieved from http://www.guided-selling.org/women-vs-men-gender-differences-in-purchase-decision-making/.van AswegenA.2015Women vs. men—Gender differences in purchase decision makingRetrieved fromhttp://www.guided-selling.org/women-vs-men-gender-differences-in-purchase-decision-making/Search in Google Scholar
Zimmer, M., & Proferes, N. (2014). A topology of Twitter research: Disciplines, methods, and ethics. Aslib Journal of Information Management, 66(3), 250–261.10.1108/AJIM-09-2013-0083ZimmerM.ProferesN.2014A topology of Twitter research: Disciplines, methods, and ethics663250261Open DOISearch in Google Scholar