Skip to main content

Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal


While there is increasing global attention to data privacy, most of their current theoretical understanding is based on research conducted in a few countries. Prior work argues that people’s cultural backgrounds might shape their privacy concerns; thus, we could expect people from different world regions to conceptualize them in diverse ways. We collected and analyzed a large-scale dataset of tweets about the #CambridgeAnalytica scandal in Spanish and English to start exploring this hypothesis. We employed word embeddings and qualitative analysis to identify which information privacy concerns are present and characterize language and regional differences in emphasis on these concerns. Our results suggest that related concepts, such as regulations, can be added to current information privacy frameworks. We also observe a greater emphasis on data collection in English than in Spanish. Additionally, data from North America exhibits a narrower focus on awareness compared to other regions under study. Our results call for more diverse sources of data and nuanced analysis of data privacy concerns around the globe.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6




  3. The authors are fairly confident of the quality of these translations because some of them are Spanish native speakers while others are English native speakers


  5. Since its release in May 2014, Botometer has served over one million requests (Davis et al. 2016) via its website ( and its Python API (




  9. Terms in Spanish were translated to English by the authors


  11. Terms in Spanish were translated to English by the authors. These terms are shown in cursive



  • Adu, Ernest K; Todorova, Nelly; and Mills, Annette (2019). Do individuals in developing countries care about personal health information privacy? an empirical investigation. In CONF-IRM 2019. Proceedings of the 2019 international conference on information resources management, auckland, new zealand, 27-29 may, 2019. Auckland, New Zealand: School of Business. University of Auckland, p. 16.

  • Aghababaei, Somayyeh; and Makrehchi, Masoud (2017). Activity-based Twitter sampling for content-based and user-centric prediction models. Human-centric Computing and Information Sciences, vol. 7, no. 1, p. 3.

  • Agüero-Torales, Marvin M.; Vilares, David; and López-Herrera, Antonio G. (2021). Discovering topics in Twitter about the COVID-19 outbreak in Spain. Procesamiento del Lenguaje Natural, vol. 66, pp. 177–190.

  • Aguerre, Carolina (2019). Digital trade in Latin America: mapping issues and approaches. Digital Policy, Regulation and Governance, vol. 21, no. 1, pp. 2–18.

  • Badawy, Adam; Ferrara, Emilio; and Lerman, Kristina (2018). Analyzing the digital traces of political manipulation: the 2016 Russian interference Twitter campaign. In ASONAM 2018. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Spain, 28-31 August, 2018. IEEE, pp. 258–265.

  • Bélanger, France; and Crossler, Robert E. (2011). Privacy in the digital age: a review of information privacy research in information systems. Management Information Systems Quarterly, MIS Quarterly, vol. 35, no. 4, pp. 1017–1042.

  • Bellman, Steven; Johnson, Eric J.; Kobrin, Stephen J.; and Lohse, Gerald L. (2004). International differences in information privacy concerns: a global survey of consumers. The Information Society, vol. 20, no. 5, pp. 313–324.

  • Benamati, John H.; Ozdemir, Zafer D.; and Jeff Smith, H. (2021). Information privacy, Cultural values, and Regulatory preferences. Journal of Global Information Management (JGIM), vol. 29, no. 3, pp. 131–164.

  • Bergsma, Shane; Dredze, Mark; Van Durme, Benjamin; Wilson, Theresa; and Yarowsky, David (2013). Broadly improving user classification via communication-based name and location clustering on twitter. In NAACL HLT 2013. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, USA, 9-14 June, 2013. Association for Computational Linguistics, pp. 1010–1019.

  • Bolukbasi, Tolga; Chang, Kai-Wei; Zou, James Y.; Saligrama, Venkatesh; and Kalai, Adam T. (2016). Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In NIPS 2016. Advances in Neural Information Processing Systems, Barcelona, Spain, 5-10 December, 2016. NY. United States: Curran Associates Inc., pp. 4349–4357.

  • Buchanan, Tom; Paine, Carina; Joinson, Adam N; and Reips, Ulf-Dietrich (2007). Development of measures of online privacy concern and protection for use on the Internet. Journal of the American Society for Information Science and Technology, vol. 58, no. 2, pp. 157–165.

  • Janice, C. Sipior; Wardm, Burke T.; and Connolly, Regina (2013). Empirically assessing the continued applicability of the IUIPC construct. Journal of Enterprise Information Management, vol. 26, no. 6, pp. 661–678.

  • Cabañas, José González; Cuevas, Ángel; and Cuevas, Rubén (2018). Unveiling and Quantifying Facebook Exploitation of Sensitive Personal Data for Advertising Purposes. In SEC’18. Proceedings of the 27th USENIX Conference on Security Symposium. USENIX Association, pp 479–495.

  • Cadwalladr, Carole; and Graham-Harrison, Emma (2018). Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach. Accessed 16 July 2020.

  • Cannataci, Joseph A. (2009). Privacy, technology law and religions across cultures. Journal of information, Law and Technology, 2009(1).

  • Cao, Jinwei; and Everard, Andrea (2008). User attitude towards instant messaging: The effect of espoused national cultural values on awareness and privacy. Journal of Global Information Technology Management, vol. 11, no. 2, pp. 30–57.

  • Chai, Sangmi (2020). Does cultural difference matter on social media? an examination of the ethical culture and information privacy concerns. Sustainability, 12(19).

  • Cho, Hichang; Rivera-Sánchez, Milagros; and Lim, Sun Sun (2009). A multinational study on online privacy: global concerns and local responses. New Media & Society, vol. 11, no. 3, pp. 395–416.

  • Cockcroft, Sophie; and Rekker, Saphira (2016). The relationship between culture and information privacy policy. Electronic Markets, vol. 26, no. 1, pp. 55–72.

  • Cohen, Jacob (1988). Statistical Power Analysis for the Behavioral Sciences hillsdale. L. Erlbaum Associates: N.J.

  • Consolvo, Sunny; Smith, Ian E.; Matthews, Tara; LaMarca, Anthony; Tabert, Jason; and Powledge, Pauline (2005). Location disclosure to social relations: why, when, & what people want to share. In CHI’05. Proceedings of the SIGCHI conference on Human factors in computing systems, Portland, Oregon, USA, 2-7 April, 2005. New York, NY. USA: Association for Computing Machinery, pp. 81–90.

  • Correia, John; and Compeau, Deborah (2017). Information Privacy Awareness (IPA): A Review of the Use, Definition and Measurement of IPA. In HICSS-50. Proceedings of the 50th Hawaii International Conference on System Sciences, Hilton Waikoloa Village, Big Island, HI, USA, 4-7 January, 2017. ScholarSpace / AIS Electronic Library (AISeL).

  • Da Veiga, Adéle (2018). An information privacy culture instrument to measure consumer privacy expectations and confidence. Information & Computer Security, vol. 26, no. 3, pp. 338–364.

  • Davis, Clayton Allen; Varol, Onur; Ferrara, Emilio; Flammini, Alessandro; and Menczer, Filippo (2016). Botornot: A system to evaluate social bots. In WWW ’16 Companion. Proceedings of the 25th International Conference Companion on World Wide Web, montréal, québec, Canada, 11-15 April, 2016. International World Wide Web Conferences Steering Committee, pp 273–274.

  • Dias Canedo, Edna; Calazans, Angelica Toffano Seidel; Masson, Eloisa Toffano Seidel Masson; Costa, Pedro Henrique Teixeira; and Lima, Fernanda (2020). Perceptions of ICT Practitioners Regarding Software Privacy. Entropy, vol. 22, no. 4, p. 429.

  • Dienlin, Tobias; and Trepte, Sabine (2015). Is the privacy paradox a relic of the past? an in-depth analysis of privacy attitudes and privacy behaviors. European Journal of Social Psychology, vol. 45, no. 3, pp. 285–297.

  • Dinev, Tamara; and Hart, Paul (2006). An extended privacy calculus model for e-commerce transactions. Information systems research, vol. 17, no. 1, pp. 61–80.

  • Ebert, Nico; Ackermann, Kurt Alexander; and Heinrich, Peter (2020). Does context in privacy communication really matter? — a survey on consumer concerns and preferences. In CHI’20. Proceedings of the 2020 CHI conference on human factors in computing systems, honolulu, HI, USA, 25-30 April, 2020. New York: Association for Computing Machinery, pp. 1–11.

  • Egelman, Serge; and Peer, Eyal (2015a). Predicting privacy and security attitudes. ACM Special Interest Group on Computers and Society. SIGCAS, vol. 45, no. 1, pp. 22–28.

  • Egelman, Serge; and Peer, Eyal (2015b). Scaling the security wall: Developing a security behavior intentions scale (sebis). In CHI’15. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea, 18-23 April, 2015. New York: Association for Computing Machinery, pp. 2873–2882.

  • Gellman, Robert (2019). Fair Information Practices: A Basic History-Version 2.19. Social Science Research Network. SSRN Electronic Journal.

  • Gerber, Nina; Gerber, Paul; and Volkamer, Melanie (2018). Explaining the privacy paradox: a systematic review of literature investigating privacy attitude and behavior. Computers & Security, vol. 77, pp. 226–261.

  • González, Felipe; Yu, Yihan; Figueroa, Andrea; López, Claudia; and Aragon, Cecilia (2019). Global reactions to the cambridge analytica scandal: a Cross-Language social media study. In WWW ’19. Companion proceedings of the 2019 world wide web conference, san francisco, USA, 13-17 May, 2019. New York: Association for Computing Machinery, pp. 799–806.

  • Hajjem, Malek; and Latiri, Chiraz (2017). Combining IR and LDA topic modeling for filtering microblogs. Procedia Computer Science, vol. 112, pp. 761–770.

  • Harborth, David; and Pape, Sebastian (2017). Privacy concerns and behavior of pokémon go players in Germany. In 12th IFIP Summer School on Privacy and Identity Management, Ispra, Italy, 3-8 September, 2017., Springer, pp. 314–329.

  • Harborth, David; and Pape, Sebastian (2018). German Translation of the Concerns for Information Privacy (CFIP) Construct. Social Science Research Network. SSRN Electronic Journal.

  • Harzing, Anne-Wil (2005). Does the use of English-language questionnaires in cross-national research obscure national differences? International Journal of Cross Cultural Management, vol. 5, no. 2, pp. 213–224.

  • Harzing, Anne-Wil (2006). Response styles in cross-national survey research: a 26-country study. International Journal of Cross Cultural Management, vol. 6, no. 2, pp. 243–266.

  • Harzing, Anne-Wil; and Maznevski, Martha (2002). The interaction between language and culture: a test of the cultural accommodation hypothesis in seven countries. Language and Intercultural Communication, vol. 2, no. 2, pp. 120–139.

  • Haynes, Winston (2013). Bonferroni correction. New York: Springer, pp. 154–154.

  • Heravi, Alireza; and Mubarak, Sameera (2018). Kim-kwang Raymond Choo Information privacy in online social networks: Uses and gratification perspective. Computers in Human Behavior, vol. 84, pp. 441–459.

  • Heredia, Brian; Prusa, Joseph D.; and Khoshgoftaar, Taghi M. (2018). The Impact of Malicious Accounts on Political Tweet Sentiment. In CIC’18. Proceedings of the 4th International Conference on Collaboration and Internet Computing, Philadelphia, PA, USA, 18-20 October, 2018. Philadelphia: IEEE, pp. 197–202.

  • Hofstede, Geert (1983). National cultures in four dimensions: a research-based theory of cultural differences among nations. International Studies of Management & Organization, vol. 13, no. 1-2, pp. 46–74.

  • Hofstede, Geert (2011). Dimensionalizing cultures: The Hofstede model in context. Online readings in psychology and culture, 2, article 8, 26.

  • Hong, Weiyin; and Thong, James Y. L. (2013). Internet privacy concerns: an integrated conceptualization and four empirical studies. Management Information Systems Quarterly. MIS Quarterly, vol. 37, no. 1, pp. 275–298.

  • Huang, Hsiao-Ying; and Bashir, Masooda (2016). Privacy by region: Evaluation online users’ privacy perceptions by geographical region. In FTC 2016. Future Technologies Conference, San Francisco, California, USA, 6-7 December, 2016. IEEE, pp. 968–977.

  • Hussein, Basel Al-Sheikh (2012). The sapir-whorf hypothesis today. Theory and Practice in Language Studies, vol. 2, no. 3, pp. 642–646.

  • Šidák, Zbynék (1967). Rectangular confidence regions for the means of multivariate normal distributions. Journal of the American Statistical Association, vol. 62, no. 318, pp. 626–633.

  • Ilyas, Sardar Haider Waseem; Soomro, Zainab Tariq; Anwar, Ahmed; Shahzad, Hamza; and Yaqub, Ussama (2020). Analyzing Brexit’s Impact Using Sentiment Analysis and Topic Modeling on Twitter Discussion. In DG.o’20. Proceedings of the 21st Annual International Conference on Digital Government Research, Seoul, Republic of Korea, 17-19 June, 2020. New York,: Association for Computing Machinery, pp. 1–6.

  • Isaak, Jim; and Hanna, Mina J. (2018). User data privacy: facebook, Cambridge Analytica, and Privacy Protection. Computer, vol. 51, no. 8, pp. 56–59.

  • Jackoway, Alan; Samet, Hanan; and Sankaranarayanan, Jagan (2011). Identification of live news events using Twitter. In LBSN ’11. Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks, Chicago, Illinois, USA, 1 November, 2011. New York: Association for Computing Machinery, pp. 25–32.

  • Jai, Tun-Min Catherine; and King, Nancy J. (2016). Privacy versus reward: Do loyalty programs increase consumers’ willingness to share personal information with third-party advertisers and data brokers? Journal of Retailing and Consumer Services, vol. 28, pp. 296–303.

  • Jha, Akshita; and Mamidi, Radhika (2017). When does a compliment become sexist? analysis and classification of ambivalent sexism using twitter data. In Proceedings of the Second Workshop on NLP and Computational Social Science, Vancouver, Canada, August, 2017. Association for Computational Linguistics, pp. 7–16.

  • Jiang, Ke; Benefield, Grace A.; Yang, Junfei; and Barnett, George (2017). Mapping Articles on China in Wikipedia An Inter-Language Semantic Network Analysis. In HICSS-50. Proceedings of the 50th Hawaii International Conference on System Sciences, Hilton Waikoloa Village, Big Island,, 4-7 January, 2017. ScholarSpace / AIS Electronic Library (AISeL), pp. 2233–2242.

  • Jozani, Mohsen; Ayaburi, Emmanuel; Myung, Ko; and Choo, Kim-Kwang Raymond (2020). Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective. Computers in Human Behavior, 107, article 106260, 15.

  • Karampela, Maria; Ouhbi, Sofia; and Isomursu, Minna (2019). Exploring users’ willingness to share their health and personal data under the prism of the new GDPR: implications in healthcare. In EMBC 2019. Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany, 23-27 July, 2019. IEEE, pp. 6509–6512.

  • Kay, Paul; and Kempton, Willett (1984). What is the Sapir-Whorf hypothesis? American anthropologist, vol. 86, no. 1, pp. 65–79.

  • Kobourov, Stephen G. (2013). Force-Directed Drawing Algorithms. In: Tamassia, R. (ed.) Handbook of Graph Drawing and Visualization: Chapman and Hall/CRC, pp. 383–408.

  • Kokolakis, Spyros (2017). Privacy attitudes and privacy behaviour: a review of current research on the privacy paradox phenomenon. Computers & security, vol. 64, pp. 122–134.

  • Krasnova, Hanna; and Veltri, Natasha F (2010). Privacy calculus on social networking sites: Explorative evidence from Germany and USA. In HICSS’10. Proceedings of the 2010 43rd Hawaii International Conference on System Sciences, Koloa, Kauai, HI, USA, 5-8 January, 2010. Washington: IEEE, pp. 1–10.

  • Kumar, V.; and Reinartz, Werner (2012). Customer privacy concerns and privacy protective responses. Berlin: Springer, pp. 279–300.

  • Kumaraguru, Ponnurangam; and Cranor, Lorrie Faith (2005). Privacy indexes: a survey of Westin’s studies. Technical Report, CMU-ISRI-5-138, Institute for Software Research International, School of Computer Science, Carnegie Mellon University, Pittsburgh. Accessed 16 July 2020.

  • Kuner, Christopher; Jerker, Dan; Svantesson, B.; Cate, Fred H.; Lynskey, Orla; Millard, Christopher; and Loideain, Nora Ni (2017). The GDPR as a chance to break down borders. International Data Privacy Law, vol. 7, no. 4, pp. 231–232.

  • Lagos, Vasileios (2017). Comparative analysis of the hashtags #RefugeesWelcome and #StopRefugees: Tweets mining and examination with the Knime Analytics Platform. Master’s thesis, University of Peloponnese.

  • Lapaire, Jean-Rémi (2018). Why content matters. zuckerberg, Vox Media and the Cambridge Analytica data leak. ANTARES: Letras e Humanidades, vol. 10, no. 20, pp. 88–110.

  • Lee, Hwansoo; Lim, Dongwon; Kim, Hyerin; Zo, Hangjung; and Ciganek, Andrew P (2015). Compensation paradox: the influence of monetary rewards on user behaviour. Behaviour & Information Technology, vol. 34, no. 1, pp. 45–56.

  • Lee, Hwansoo; Wong, Siew Fan; Oh, Jungjoo; and Chang, Younghoon (2019). Information privacy concerns and demographic characteristics: Data from a Korean media panel survey. Government Information Quarterly, vol. 36, no. 2, pp. 294–303.

  • Leetaru, Kalev (2019). Is Twitter’s Spritzer Stream Really A Nearly Perfect 1% Sample Of Its Firehose?, Forbes. Accessed 16 July 2020.

  • Leidner, Dorothy E; and Kayworth, Timothy (2006). A review of culture in information systems research: Toward a theory of information technology culture conflict. Management Information Systems Quarterly. MIS Quarterly, vol. 30, no. 2, pp. 357–399.

  • Li, Yao; Kobsa, Alfred; Knijnenburg, Bart P; and Nguyen, MH Carolyn (2017). Cross-cultural privacy prediction. Proceedings on Privacy Enhancing Technologies, vol. 2017, no. 2, pp. 113–132.

  • Li, Yao; Rho, Eugenia Ha Rim; and Kobsa, Alfred (2020). Cultural differences in the effects of contextual factors and privacy concerns on users’ privacy decision on social networking sites. Behaviour & Information Technology, 1–23.

  • Lin, Yu-Wei (2018). #DeleteFacebook is still feeding the beast–but there are ways to overcome surveillance capitalism, The Conversation Trust. Accessed 16 July 2020.

  • Liu, Dapeng; and Carter, Lemuria (2018). Impact of citizens’ privacy concerns on e-government adoption. In DG-o’18. Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age, Delft, 30 May - 1 June 2018, pp. 1–6.

  • Malhotra, Naresh K.; Kim, Sung S.; and Agarwal, James (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, vol. 15, no. 4, pp. 336–355.

  • Markos, Ereni; Milne, George R.; and Peltier, James W (2017). Information sensitivity and willingness to provide continua: a comparative privacy study of the United States and Brazil. Journal of Public Policy & Marketing, vol. 36, no. 1, pp. 79–96.

  • Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg S; and Dean, Jeff (2013). Distributed representations of words and phrases and their compositionality. In NIPS 2013. Advances in neural information processing systems, Lake Tahoe, 5-10 December 2013, pp. 3111–3119.

  • Mirchandani, Maya (2018). To delete, or not to #deleteFacebook, that is the question, The Wire. Accessed 16 July 2020.

  • Mohammed, Zareef A.; and Tejay, Gurvirender P. (2017). Examining Privacy Concerns and Ecommerce Adoption in Developing Countries. Computers & Security, 67(C), 254–265.

  • Morstatter, Fred; Pfeffer, Jürgen; and Liu, Huan (2014). When is it biased?: assessing the representativeness of twitter’s streaming API. In WWW’14. Proceedings of the 23rd international conference on world wide web, Seoul, Korea, 7-11 April, 2014. New York: Association for Computing Machinery, pp. 555–556.

  • Morstatter, Fred; Pfeffer, Jürgen; Liu, Huan; and Carley, Kathleen M. (2013). Is the sample good enough? comparing data from twitter’s streaming api with twitter’s firehose. In ICWSM-2013. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Cambridge, Massachusetts, USA, 8–11 July, 2013. Menlo Park: AAAI Press, pp. 400–408.

  • Morton, Anthony; and Angela Sasse, M. (2014). Desperately seeking assurances: Segmenting users by their information-seeking preferences. In PST 2014. Proceedings of the 12th International Conference on Privacy, Security and Trust, Toronto, Canada, 23-24 July, 2014. IEEE, pp. 102–111.

  • Motiwalla, Luvai F.; Li, Xiaobai (Bob); and Liu, Xiaoping (2014). Privacy paradox: Does stated privacy concerns translate into the valuation of personal information? In PACIS 2014. Proceedings of the 18th Pacific Asia Conference on Information Systems, Chengdu, China, 24-28 June, 2014. p. 281 Article 281.

  • Newell, Patricia Brierley (1995). Perspectives on privacy. Journal of Environmental Psychology, vol. 15, no. 2, pp. 87–104.

  • Nov, Oded; and Wattal, Sunil (2009). Social computing privacy concerns: antecedents and effects. In CHI’09. Proceedings of the SIGCHI conference on human factors in computing systems, Boston, MA, USA, 4-9 April, 2009. New York,: Association for Computing Machinery, pp. 333–336.

  • O’Connor, Brendan; Balasubramanyan, Ramnath; Routledge, Bryan R.; and Smith, Noah A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In ICWSM-2010. Proceedings of the fourth international AAAI conference on weblogs and social media, Washington DC, USA, 23–26 May, 2010. Menlo Park: AAAI Press, pp. 122–129.

  • Oghazi, Pejvak; Schultheiss, Rakel; Chirumalla, Koteshwar; Kalmer, Nicolas Philipp; and Rad, Fakhreddin F. (2020). User self-disclosure on social network sites: A cross-cultural study on Facebook’s privacy concepts. Journal of Business Research, vol. 112, pp. 531–540.

  • Okazaki, Shintaro; Eisend, Martin; Plangger, Kirk; de Ruyter, Ko; and Grewal, Dhruv (2020). Understanding the Strategic Consequences of Customer Privacy Concerns: A Meta-Analytic Review. Journal of Retailing, vol. 96, no. 4, pp. 458–473.

  • Palos-Sanchez, Pedro; Hernandez-Mogollon, José; and Campon-Cerro, Ana (2017). The behavioral response to location based services: an examination of the influence of social and environmental benefits, and privacy. Sustainability, 9(11), article 1988.

  • Raber, Frederic; and Krüger, Antonio (2018). Privacy Perceiver: Using Social Network Posts to Derive Users’ Privacy Measures. In UMAP’18. Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, Singapore, Singapore, 8-11 July, 2018. New York: Association for Computing Machinery, pp. 227–232.

  • Rho, Eugenia Ha Rim; Mark, Gloria; and Mazmanian, Melissa (2018). Fostering Civil Discourse Online: Linguistic Behavior in Comments of #MeToo Articles across Political Perspectives. In CSCW 2018. Proceedings of the ACM on Human-Computer Interaction. Association for Computing Machinery, 2, article 147, 28.

  • Rowan, Mark; and Dehlinger, Josh (2014). Observed gender differences in privacy concerns and behaviors of mobile device end users. Procedia Computer Science, vol. 37, pp. 340–347.

  • Schneble, Christophe Olivier; Elger, Bernice Simone; and Shaw, David (2018). The Cambridge Analytica affair and Internet-mediated research. European Molecular Biology Organization. EMBO reports, 19, no. 8, article e46579.

  • Shen, Chien-Wen; and Kuo, Chin-Jin (2014). Analysis of social influence and information dissemination in social media: The case of Twitter. In CISIM. Proceedings of the 13th IFIP International Conference on Computer Information Systems and Industrial Management, Ho Chi Minh City, Vietnam, November 2014. Berlin: Springer Berlin Heidelberg, pp. 526–534.

  • Singh, Loitongbam Gyanendro; Anil, Akash; and Singh, Sanasam Ranbir (2020). SHE: Sentiment Hashtag embedding through multitask learning. IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 417–424.

  • Smith, H. Jeff; Dinev, Tamara; and Xu, Heng (2011). Information privacy research: an interdisciplinary review. Management Information Systems Quarterly. MIS Quarterly, vol. 35, no. 4, pp. 989–1016.

  • Smith, H Jeff; Milberg, Sandra J.; and Burke, Sandra J. (1996). Information privacy: measuring individuals’ concerns about organizational practices. Management Information Systems Quarterly, MIS Quarterly, pp. 167–196.

  • Solon, Olivia; and Laughland, Oliver (2018). Cambridge Analytica closing after Facebook data harvesting scandal, The Guardian. Accessed 16 July 2020.

  • Stewart, Kathy A.; and Segars, Albert H. (2002). An empirical examination of the concern for information privacy instrument. Information Systems Research, vol. 13, no. 1, pp. 36–49.

  • Tavoschi, Lara; Quattrone, Filippo; D’Andrea, Eleonora; Ducange, Pietro; Vabanesi, Marco; Marcelloni, Francesco; and Lopalco, Pier Luigi (2020). Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy. Human Vaccines & Immunotherapeutics, 16(5), 1062–1069. PMID 32118519.

  • Terlutter, Ralf; Diehl, Sandra; and Mueller, Barbara (2006). The GLOBE study—applicability of a new typology of cultural dimensions for cross-cultural marketing and advertising research. International advertising and communication, 419–438.

  • Torabi, Sadegh; and Beznosov, Konstantin (2016). Sharing health information on facebook: practices, Preferences, and Risk Perceptions of North American Users. In SOUPS 2016. Twelfth Symposium on Usable Privacy and Security. USENIX Association, 301–320.

  • Tumasjan, Andranik; Sprenger, Timm O.; Sandner, Philipp G.; and Welpe, Isabell M. (2010). Predicting elections with twitter: What 140 characters reveal about political sentiment. In ICWSM-2010. Proceedings of the Fourth international AAAI conference on weblogs and social media, Washington DC, USA, 23–26 May, 2010. Menlo Park, California: AAAI Press.

  • United Nations Conference on Trade and Development (2020). Data Protection and Privacy Legislation Worldwide. Accessed 12 February 2020.

  • Ur, Blase; and Wang, Yang (2013). A cross-cultural framework for protecting user privacy in online social media. In WWW ’13. Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 13-17 May 2013. New York: Association for Computing Machinery, pp. 755–762.

  • Van Slyke, Craig; Shim, J. T.; Johnson, Richard; and Jiang, James J. (2006). Concern for information privacy and online consumer purchasing. Journal of the Association for Information Systems, vol. 7, no. 6, pp. 415–444.

  • Varol, Onur; Ferrara, Emilio; Davis, Clayton A.; Menczer, Filippo; and Flammini, Alessandro (2017). Online human-bot interactions: detection, estimation, and characterization. In ICWSM-2017. Proceedings of the eleventh international AAAI conference on web and social media, Montréal, Québec, Canada, 15–18 May, 2017. Palo Alto: AAAI Press, pp. 280–289.

  • Venturini, Tommaso; and Rogers, Richard (2019). “API-Based Research” or How can Digital Sociology and Journalism Studies Learn from the Facebook and Cambridge Analytica Data Breach. Digital Journalism, pp. 1–9.

  • Viera, Anthony J.; Garrett, Joanne M. et al. (2005). Understanding interobserver agreement: the kappa statistic. Family Medicine, vol. 37, no. 5, pp. 360–363.

  • Vitak, Jessica; Blasiola, Stacy; Patil, Sameer; and Litt, Eden (2015). Balancing audience and privacy tensions on social network sites: Strategies of highly engaged users. International Journal of Communication, vol. 9, pp. 1485–1504.

  • Vitkauskaite, Elena (2010). Overview of research on cross-cultural impact on social networking sites. Economics and Management, vol. 15, pp. 844–848.

  • Wang, Bin; Wang, Angela; Chen, Fenxiao; Wang, Yuncheng; and Jay Kuo, C.-C. (2019). Evaluating word embedding models: methods and experimental results. APSIPA Transactions on Signal and Information Processing, 8, article e19, 14.

  • Wang, Haizhou; and Song, Mingzhou (2011). Ckmeans. 1d. dp: optimal k-means clustering in one dimension by dynamic programming. The R Journal, vol. 3, no. 2, pp. 29–33.

  • Williams, Michael; and Moser, Tami (2019). The art of coding and thematic exploration in qualitative research. International Management Review, vol. 15, no. 1, pp. 45–55.

  • Wisniewski, Pamela J.; Najmul Islam, A. K. M.; Knijnenburg, Bart P.; and Patil, Sameer (2015). Give Social Network Users the Privacy They Want. In CSCW’15. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, Vancouver, BC, Canada, 14-18 March, 2015. New York: Association for Computing Machinery, pp. 1427–1441.

  • Wisniewski, Pamela J.; Knijnenburg, Bart P.; and Lipford, Heather Richter (2017). Making privacy personal: Profiling social network users to inform privacy education and nudging. International Journal of Human-Computer Studies, vol. 98, pp. 95–108.

  • Woodruff, Allison; Pihur, Vasyl; Consolvo, Sunny; Brandimarte, Laura; and Acquisti, Alessandro (2014). Would a Privacy Fundamentalist Sell Their DNA for $1000... If Nothing Bad Happened as a Result? The Westin Categories, Behavioral Intentions, and Consequences. In SOUPS 2014. Proceedings of the 10th Symposium On Usable Privacy and Security, Menlo Park, CA, USA, 9-11 July, 2014. USENIX Association, pp. 1–18.

  • Yang, Hongwei (2013). Young American consumers’ online privacy concerns, trust, risk, social media use, and regulatory support. Journal of New Communications Research, vol. 5, no. 1, pp. 1–30.

  • Yang, Kai-Cheng; Varol, Onur; Davis, Clayton A.; Ferrara, Emilio; Flammini, Alessandro; and Menczer, Filippo (2019). Arming the public with artificial intelligence to counter social bots. Human Behavior and Emerging Technologies, vol. 1, no. 1, pp. 48–61.

  • Yaqub, Ussama; Chun, Soon Ae; Atluri, Vijayalakshmi; and Vaidya, Jaideep (2017). Analysis of political discourse on twitter in the context of the 2016 US presidential elections. Government Information Quarterly, vol. 34, no. 4, pp. 613–626.

  • Yeşiltaş, Gökçe; and Güngör, Tunga (2020). Intrinsic and Extrinsic Evaluation of Word Embedding Models. In ASYU 2020. Proceedings of the 2020 Innovations in intelligent systems and applications conference, Istanbul, 15-17 October 2020, pp. 1–6.

  • Yun, Haejung; Lee, Gwanhoo; and Kim, Dan J. (2019). A chronological review of empirical research on personal information privacy concerns: an analysis of contexts and research constructs. Information & Management, vol. 56, no. 4, pp. 570–601.

  • Zarifis, Alex; Ingham, Richard; and Kroenung, Julia (2019). Exploring the language of the sharing economy: Building trust and reducing privacy concern on Airbnb in German and English. Cogent Business & Management, 6(1), article 1666641.

  • Zhao, Jieyu; Zhou, Yichao; Li, Zeyu; Wang, Wei; and Chang, Kai-Wei (2018). Learning Gender-Neutral word embeddings. In EMNLP 2018. Proceedings of the 2018 conference on empirical methods in natural language processing, brussels, belgium, 31 october – 4 november, 2018. Association for Computational Linguistics, pp. 4847–4853.

  • Zou, Yixin; Mhaidli, Abraham H.; McCall, Austin; and Schaub, Florian (2018). “I’ve Got nothing to lose”: Consumers’ risk perceptions and protective actions after the equifax data breach. In SOUPS 2018. Fourteenth symposium on usable privacy and security, baltimore, MD, USA, 12–14 August, 2018. Baltimore: USENIX, Association, pp. 197–216.

  • Zukowski, Tomasz; and Brown, Irwin (2007). Examining the influence of demographic factors on internet users’ information privacy concerns. In SAICSIT ’07. Proceedings of the 2007 Annual Conference of the South African Institute of Computer Scientists and Information Technologists, Port Elizabeth, South Africa, 2-3 October 2007. New York: Association for Computing Machinery, pp. 197–204.

Download references


The authors want to thank Francisco Tobar, MSc. Computer Science student at Universidad Técnica Federico Santa María, for helping us to strengthen our findings through statistical analysis. Moreover, we acknowledge anonymous reviewers for insightful comments that helped us revise and refine the paper.


This collaboration was possible thanks to the support of the Fulbright Program, under a 2017-18 Fulbright Fellowship award. This work was also partially funded by CONICYT Chile, under grant Conicyt/Fondecyt Iniciación/11161026. The first author acknowledges the support of the PIIC program from Universidad Técnica Federico Santa María and CONICYT-PFCHA/MagísterNacional/2019-22190332.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Claudia López.

Ethics declarations

Conflict of Interests

The authors declare that there is no conflict of interest regarding the publication of this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

González-Pizarro, F., Figueroa, A., López, C. et al. Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data Scandal. Comput Supported Coop Work 31, 33–77 (2022).

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Online privacy
  • Twitter
  • Word embedding
  • Content analysis