Elucidating the role of emotion in privacy-concerns: A text-Convolutional Neural Network (Text-CNN)-based tweets analysis of contact tracing apps


  • Mihir Mehta Indian Institute of Management Raipur
  • Sourya Joyee De Indian Institute of Management Raipur
  • Manojit Chattopadhyay Indian Institute of Management Raipur




Privacy Calculus Theory, emotion analysis of tweets, contact tracing apps, perceived privacy risks, perceived privacy protections, text-Convolutional Network


The extant contact tracing privacy literature is yet to explore the significance of user emotions in privacy-related decision-making such as whether to use such potentially privacy-invasive apps. Using social media analytics, the present study examines users’ privacy-related emotions stimulated by privacy-related aspects of contact tracing apps. A text-Convolutional Neural Network (Text-CNN)-based emotion analysis of tweets on the Indian contact tracing app Aarogya Setu and its Singaporean counterpart TraceTogether conducted in the paper reveals that users expressed negative privacy-related emotions towards these apps indicating high levels of perceived privacy risks and the perceived lack of privacy protection. For TraceTogether, users have also exhibited positive emotions to appreciate the steps taken by the government to protect their privacy. Based on these findings, the government/data controllers can devise strategies to assuage users’ negative emotions and promote positive emotions to encourage the adoption of contact tracing apps. This work incorporates privacy related emotions as key informants about user privacy concerns within the Privacy Calculus Theory. By relying on candid user opinions available through rich but inexpensive user-generated content, the research provides a quick, reliable, and cost-effective approach to study potential app users’ emotions to gain insights into privacy concerns related to any e-governance platform.

Author Biographies

Mihir Mehta, Indian Institute of Management Raipur

Mihir P Mehta is pursuing MBA at IIM Raipur after the completion of his Bachelor of Technology. He is interested in computer application in business and management.

Sourya Joyee De, Indian Institute of Management Raipur

Sourya Joyee De is an Assistant Professor in IT and Systems area at Indian Institute of Management Raipur, India. She is a Fellow of Indian Institute of Management Calcutta (Ph.D.). Prior to joining IIM Raipur, Sourya has held research positions at INRIA Grenoble Rhone-Alpes and LORIA-CNRS-INRIA Nancy Grand-Est, France for close to four years. Her research has been funded by the French ANR project BIOPRIV, CISCO San Jose, U.S.A., Samsung GRO Grant, INRIA Project Lab CAPPRIS, and the Grand-Est Region, France. Sourya was also a Visiting Scientist at Indian Statistical Institute Kolkata, India. Her research interests include information privacy and security. Her research has been published at various reputed journals and conferences. She has also published two books on privacy with Morgan & Claypool Publishers, San Rafael, CA, U.S.A.


Anderson, C.L. & Agarwal, R. (2011) The digitization of healthcare: boundary risks, emotion, and consumer willingness to disclose personal health information. Information Systems Research, 22, 469–490.

Arnold, M. B. (1960). Emotion and personality (Vol. I & II). New York. Columbia University Press.

Azad, M. A., Arshad, J., Akmal, S. M. A., Riaz, F., Abdullah, S., Imran, M., & Ahmad, F. (2020). A first look at privacy analysis of COVID-19 contact tracing mobile applications. IEEE Internet of Things Journal. https://doi.org/10.48550/arXiv.2006.13354

Balapour, A., Nikkhah, H. R., & Sabherwal, R. (2020). Mobile application security: Role of perceived privacy as the predictor of security perceptions. International Journal of Information Management, 52, 102063.

Barnard, L. (2014). The cost of creepiness: How online behavioral advertising affects consumer purchase intention (Doctoral dissertation, The University of North Carolina at Chapel Hill).

Baumgärtner, L., Dmitrienko, A., Freisleben, B., Gruler, A., Höchst, J., Kühlberg, J., ... & Uhl, C. (2020). Mind the GAP: Security & privacy risks of contact tracing apps. In 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 458-467.

Beldad, A., De Jong, M., & Steehouder, M. (2011). I trust not therefore it must be risky: Determinants of the perceived risks of disclosing personal data for e-government transactions. Computers in Human Behavior, 27(6), 2233-2242.

Bengio, Y., Ippolito, D., Janda, R., Jarvie, M., Prud'homme, B., Rousseau, J. F., ... & Yu, Y. W. (2021). Inherent privacy limitations of decentralized contact tracing apps. Journal of the American Medical Informatics Association, 28(1), 193-195.

Bollen, J., Mao, H., & Pepe, A. (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the International AAAI Conference on Web and Social Media, 5(1).

Buchanan, T., Paine, C., Joinson, A. N., & Reips, U. D. (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, 58(2), 157-165.

Chan, E. Y., & Saqib, N. U. (2021). Privacy concerns can explain unwillingness to download and use contact tracing apps when COVID-19 concerns are high. Computers in Human Behavior, 119, 106718.

Cho, H., Ippolito, D., & Yu, Y. W. (2020). Contact tracing mobile apps for COVID-19: Privacy considerations and related trade-offs. arXiv preprint arXiv:2003.11511.

Church, K., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22-29.

Clore, G. L., Gasper, K., & Garvin, E. (2001). Affect as information. Handbook of Affect and social Cognition, 121-144. Routledge, London, UK.

Crable, E., & Sena, M. (2020). Exploring Sentiment Towards Contact Tracing. In Proceedings of the Conference on Information Systems Applied Research, Vol. 2167, p. 1508.

Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organization Science, 10(1), 104-115.

Damasio, A. R. (1994). Descartes’ error: Emotion, rationality and the human brain. Putman, New York, USA.

Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61-80.

Dinev, T., Hart, P., & Mullen, M. R. (2008). Internet privacy concerns and beliefs about government surveillance–An empirical investigation. The Journal of Strategic Information Systems, 17(3), 214-233.

Ekman P. (2007). Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. Holt, New York, USA.

Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4), 169-200.

Fahey, R. A., & Hino, A. (2020). COVID-19, digital privacy, and the social limits on data-focused public health responses. International Journal of Information Management, 55, 102181.

Fan, W., & Gordon, M. D. (2014). The power of social media analytics. Communications of the ACM, 57(6), 74-81.

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: a perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474.

Featherman, M. S., Miyazaki, A. D., & Sprott, D. E. (2010). Reducing online privacy risk to facilitate e‐service adoption: the influence of perceived ease of use and corporate credibility. Journal of Services Marketing, 24(3), 219-229

Fiesler, C., & Hallinan, B. (2018). "We Are the Product" Public Reactions to Online Data Sharing and Privacy Controversies in the Media. In Proceedings of the 2018 CHI Conference on Human Factors In Computing Systems, 1-13.

Fox, G., Clohessy, T., van der Werff, L., Rosati, P., & Lynn, T. (2021). Exploring the competing influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing mobile applications. Computers in Human Behavior, 121, 106806.

Frijda, N. H. (1994). Varieties of affect: Emotions and episodes, moods, and sentiments. In P. Ekman & R. J. Davidson (Eds.), The nature of emotion (pp. 59–67). New York: Oxford University Press.

Georgieva, I., Beaunoyer, E., & Guitton, M. J. (2021). Ensuring social acceptability of technological tracking in the COVID-19 context. Computers in Human Behavior, 116, 106639.

Giachanou, A., & Crestani, F. (2016). Like it or not: A survey of twitter sentiment analysis methods. ACM Computing Surveys (CSUR), 49(2), 1-41.

González, F., Figueroa, A., López, C., & Aragon, C. (2019a, November). Information Privacy Opinions on Twitter: A Cross-Language Study. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing,190-194.

González, F., Yu, Y., Figueroa, A., López, C., & Aragon, C. (2019b, May). Global reactions to the Cambridge analytica scandal: A cross-language social media study. In Companion Proceedings of the 2019 World Wide Web Conference, 799-806.

Greene, J., & Haidt, J. (2002). How (and where) does moral judgment work?. Trends in Cognitive Sciences, 6(12), 517-523.

Gu, J., Xu, Y. C., Xu, H., Zhang, C., & Ling, H. (2017). Privacy concerns for mobile app download: An elaboration likelihood model perspective. Decision Support Systems, 94, 19-28.

Gutierrez, A., O'Leary, S., Rana, N. P., Dwivedi, Y. K., & Calle, T. (2019). Using privacy calculus theory to explore entrepreneurial directions in mobile location-based advertising: Identifying intrusiveness as the critical risk factor. Computers in Human Behavior, 95, 295-306.

Horvath, L., Banducci, S., & James, O. (2020). Citizens’ attitudes to contact tracing apps. Journal of Experimental Political Science, 1-13.

Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1(1), 45-71.

Johnson, R., & Zhang, T. (2015). Effective use of word order for text categorization with convolutional neural networks. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015 (p. 103).

Kehr, F., Kowatsch, T., Wentzel, D., & Fleisch, E. (2015). Blissfully ignorant: the effects of general privacy concerns, general institutional trust, and affect in the privacy calculus. Information Systems Journal, 25(6), 607-635.

Kehr, F., Wentzel, D., & Mayer, P. (2013). Rethinking the Privacy Calculus: On the Role of Dispositional Factors and Affect. In proceedings of the thirty-fourth International Conference on Information Systems, 1-10.

Keith, M. J., Thompson, S. C., Hale, J., Lowry, P. B., & Greer, C. (2013). Information disclosure on mobile devices: Re-examining privacy calculus with actual user behavior. International Journal of Human-Computer Studies, 71(12), 1163-1173.

Keltner, D., Lerner J. S. (2010). Emotion. In The Handbook of Social Psychology, Vol. 1, ed. Gilbert, D. T., Fiske, S. T., Lindzey, G., pp. 317–52. Wiley, Hoboken, NJ, USA.

Keltner, D., Oatley, K., & Jenkins, J. M. (2014). Understanding emotions. Wiley, Hoboken, NJ, USA.

Kim, D. J., Ferrin, D. L., & Rao, H. R. (2009). Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research, 20(2), 237-257.

Leith, D. J., & Farrell, S. (2020, October). Coronavirus contact tracing app privacy: What data is shared by the Singapore OpenTrace app? In International Conference on Security and Privacy in Communication Systems, 80-96. Cham, Switzerland: Springer.

Lerner, J. S., Li, Y., Valdesolo, P., & Kassam, K. S. (2015). Emotion and decision making. Annual Review of Psychology, 66, 799-823.

Li, H., Luo, X. R., Zhang, J., & Xu, H. (2017). Resolving the privacy paradox: Toward a cognitive appraisal and emotion approach to online privacy behaviors. Information & Management, 54(8), 1012-1022.

Li, H., Sarathy, R., & Xu, H. (2011). The role of affect and cognition on online consumers' decision to disclose personal information to unfamiliar online vendors. Decision Support Systems, 51(3), 434-445.

Li, H., Sarathy, R., & Zhang, J. (2008). The role of emotions in shaping consumers’ privacy beliefs about unfamiliar online vendors. Journal of Information Privacy and Security, 4(3), 36-62.

Li, Y. (2012). Theories in online information privacy research: A critical review and an integrated framework. Decision Support Systems, 54(1), 471-481.

Li, Y. (2014). The impact of disposition to privacy, website reputation and website familiarity on information privacy concerns. Decision Support Systems, 57, 343-354.

Lin, J., Carter, L., & Liu, D. (2021). Privacy concerns and digital government: exploring citizen willingness to adopt the COVIDSafe app. European Journal of Information Systems, 30(4), 1-14.

Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N. (2001). Risk as feelings. Psychological Bulletin, 127(2), 267.

Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336-355.

Masur, P. K., & Scharkow, M. (2016). Disclosure management on social network sites: Individual privacy perceptions and user-directed privacy strategies. Social Media+ Society, 2(1), 2056305116634368.

Mohammad, S. (2012). # Emotional tweets. In * SEM 2012: The First Joint Conference on Lexical and Computational Semantics–Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), 246-255).

Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010), 1320-1326.

Pang, B., & Lee, L. (2009). Opinion mining and sentiment analysis. Computational Linguistics, 35(2), 311-312.

Peng, H., Li, J., He, Y., Liu, Y., Bao, M., Wang, L., Song, Y., & Yang, Q., 2018, April. Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In Proceedings of the 2018 World Wide Web Conference (pp. 1063-1072).

Praveen, S. V., & Ittamalla, R. (2021). Analyzing Indian citizen's perspective towards government using wearable sensors to tackle COVID-19 crisis—A text analytics study. Health Policy and Technology, 10(2), 100521.

Praveen, S. V., Ittamalla, R., & Subramanian, D. (2020a). Challenges in successful implementation of Digital contact tracing to curb COVID-19 from global citizen’s perspective: A text analysis study. International Journal of Pervasive Computing and Communications, 1-8.

Praveen, S. V., Ittamalla, R., & Subramanian, D. (2020b). How optimistic do citizens feel about digital contact tracing? –Perspectives from developing countries. International Journal of Pervasive Computing and Communications, 1-9.

Raber, F., & Krüger, A. (2018, July). Privacy perceiver: Using social network posts to derive users' privacy measures. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, 227-232. ACM, New York, USA.

Rathore, A. K., & Ilavarasan, P. V. (2020). Pre-and post-launch emotions in new product development: Insights from twitter analytics of three products. International Journal of Information Management, 50, 111-127.

Rathore, A. K., Maurya, D., & Srivastava, A. K. (2021). Do policymakers use social media for policy design? A Twitter analytics approach. Australasian Journal of Information Systems, 25. https://doi.org/10.3127/ajis.v25i0.2965

Rekanar, K., O’Keeffe, I. R., Buckley, S., Abbas, M., Beecham, S., Chochlov, M., ... & Buckley, J. (2021). Sentiment analysis of user feedback on the HSE’s Covid-19 contact tracing app. Irish Journal of Medical Science, 191(1), 103-112.

Rowe, F. (2020). Contact tracing apps and values dilemmas: A privacy paradox in a neo-liberal world. International Journal of Information Management, 55, 102178.

Shaw, N., & Sergueeva, K. (2019). The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value. International Journal of Information Management, 45, 44-55.

Simko, L., Chang, J. L., Jiang, M., Calo, R., Roesner, F., & Kohno, T. (2020). COVID-19 contact tracing and privacy: A longitudinal study of public opinion. arXiv preprint arXiv:2012.01553, 1-37.

Singh, J. P., Dwivedi, Y. K., Rana, N. P., Kumar, A., & Kapoor, K. K. (2019). Event classification and location prediction from tweets during disasters. Annals of Operations Research, 283(1), 737-757.

Smith, C. A. & Kirby, L. (2000) Consequences require antecedents: Toward a process model of emotion elicitation. In Forgas, J. P. (Ed.) Feeling and Thinking: The role of affect in social cognition. Cambridge University Press.

Stieglitz, S., Dang-Xuan, L., Bruns, A., & Neuberger, C. (2014). Social media analytics-an interdisciplinary approach and its implications for information systems. Business & Information Systems Engineering, 6(2), 89-96.

Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International Journal of Information Management, 39, 156-168.

Sun, Y., Wang, N., Shen, X. L., & Zhang, J. X. (2015). Location information disclosure in location-based social network services: Privacy calculus, benefit structure, and gender differences. Computers in Human Behavior, 52, 278-292.

Tsytsarau, M., & Palpanas, T. (2012). Survey on mining subjective data on the web. Data Mining and Knowledge Discovery, 24(3), 478-514.

Ur, B., & Wang, Y. (2013, May). A cross-cultural framework for protecting user privacy in online social media. In Proceedings of the 22nd International Conference on World Wide Web, 755-762. ACM, New York, USA.

Ur, B., Leon, P. G., Cranor, L. F., Shay, R., & Wang, Y. (2012, July). Smart, useful, scary, creepy: perceptions of online behavioral advertising. In Proceedings of The Eighth Symposium on Usable Privacy and Security, 1-15. ACM, New York, USA.

Wakefield, R. (2013). The influence of user affect in online information disclosure. The Journal of Strategic Information Systems, 22(2), 157-174.

Walrave, M., Waeterloos, C., & Ponnet, K. (2020). Adoption of a contact tracing app for containing COVID-19: a health belief model approach. JMIR Public Health and Surveillance, 6(3), e20572, 1-10.

Watson, L., & Spence, M. T. (2007). Causes and consequences of emotions on consumer behaviour: A review and integrative cognitive appraisal theory. European Journal of Marketing, 41(5/6), 487-511.

Wen, H., Zhao, Q., Lin, Z., Xuan, D., & Shroff, N. (2020, October). A study of the privacy of covid-19 contact tracing apps. In International Conference on Security and Privacy in Communication Systems, 297-317. Springer, Cham, Switzerland.

Wigan, M. (2020). Rethinking IT Professional Ethics: Classical and Current Contexts. Australasian Journal of Information Systems, 24.


Wildenauer, M. (2020). The Shared Responsibility Model: Levers of Influence and Loci of Control to aid Regulation of Ethical Behaviour in Technology Platform Companies. Australasian Journal of Information Systems, 24.


Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42-52.

Xu, H., Teo, H. H., & Tan, B. C. (2005). Predicting the adoption of location-based services: The role of trust and perceived privacy risk. In 26th International Conference on Information Systems, ICIS 2005 (pp. 897-910).

Zeng, D., Chen, H., Lusch, R., & Li, S. H. (2010). Social media analytics and intelligence. IEEE Intelligent Systems, 25(6), 13-16.

Zhang, B., & Xu, H. (2016, February). Privacy nudges for mobile applications: Effects on the creepiness emotion and privacy attitudes. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing (pp. 1676-1690).




How to Cite

Mehta, M. ., De, S. J. ., & Chattopadhyay, M. (2022). Elucidating the role of emotion in privacy-concerns: A text-Convolutional Neural Network (Text-CNN)-based tweets analysis of contact tracing apps. Australasian Journal of Information Systems, 26. https://doi.org/10.3127/ajis.v26i0.3687



Research Articles