Lihi Idan
ABSTRACT
The ability to successfully infer private behavioral intentions using publicly-available digital records has a far-reaching impact. Unlike other private attributes such as demographic attributes, an intention oftentimes leads to a near-future behavior. Prior knowledge about such future behaviors can be seen as “actionable intelligence” and constitutes a significantly bigger risk to users’ privacy than knowledge of non-behavioral attributes. In this paper, we present a novel, multidisciplinary methodology for behavioral-intention inference. Using Bayesian-Networks, we model a behavioral intention using a set of causes that influence the intention’s formation, a set of effects that are caused by the intention, and various dependency relations within and between those sets. Unlike the methodologies used in prior attribute-inference works, which are oftentimes tailored to a single target attribute, our methods can be applied to different types of intentions from a diverse set of domains as we demonstrate by applying our model to multiple real-world intention-inference tasks.
- Eiji Aramaki, Sachiko Maskawa, and Mizuki Morita. 2011. Twitter catches the flu: detecting influenza epidemics using Twitter. In International Conference on Empirical Methods in Natural Language Processing.Google Scholar
- Samed Atouati, Xiao Lu, and Mauro Sozio. 2020. Negative Purchase Intent Identification in Twitter. In International World Wide Web Conference.Google Scholar
- Pamela J Brink and Kristi Ferguson. 1998. The decision to lose weight. Western Journal of Nursing Research 20, 1 (1998), 84–102.Google ScholarCross Ref
- Jie Cheng, David A. Bell, and Weiru Liu. 1997. Learning Belief Networks from Data: An Information Theory Based Approach. In International Conference on Information and Knowledge Management. 325–331.Google ScholarDigital Library
- M. Conover 2011. Predicting the political alignment of twitter users. In International Conference on Social Computing and Networking.Google ScholarCross Ref
- Alessandro Cossard, Gianmarco De Francisci Morales, Kyriaki Kalimeri, Yelena Mejova, Daniela Paolotti, and Michele Starnini. 2020. Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter. In International Conference on Web and Social Media.Google Scholar
- Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. Predicting Depression via Social Media. In International Conference on Web and Social Media.Google Scholar
- P. Ekman. 1992. An argument for basic emotions. Cognition & emotion 6, 3-4 (1992).Google Scholar
- Martin Fishbein. 2000. The role of theory in HIV prevention. AIDS care 12, 3 (2000), 273–278.Google ScholarCross Ref
- Jennifer Golbeck, Cristina Robles, Michon Edmondson, and Karen Turner. 2011. Predicting Personality from Twitter. In International Conference on Social Computing and Networking.Google ScholarCross Ref
- Jennifer Golbeck, Cristina Robles, and Karen Turner. 2011. Predicting personality with social media. In SIGCHI.Google Scholar
- Vineet Gupta, Devesh Varshney, Harsh Jhamtani, Deepam Kedia, and Shweta Karwa. 2014. Identifying purchase intent from social posts. In International Conference on Web and Social Media.Google Scholar
- Stuart Hampshire and H Hart. 1958. Decision, intention and certainty. Mind 67(1958), 1–12.Google ScholarCross Ref
- Matthew J Hornsey, Emily A Harris, and Kelly S Fielding. 2018. The Psychological Roots of Anti-Vaccination Attitudes: A 24-Nation Investigation. Health Psychology 37, 4 (2018), 307.Google ScholarCross Ref
- Xiaolei Huang, Michael C Smith, Michael J Paul, Dmytro Ryzhkov, Sandra Crouse Quinn, David A Broniatowski, and Mark Dredze. 2017. Examining Patterns of Influenza Vaccination in Social Media. In AAAI Conference on Artificial Intelligence.Google Scholar
- Kokil Jaidka 2018. Facebook vs. Twitter: Cross-Platform Differences in Self-disclosure and Trait Prediction. In International Conference on Web and Social Media.Google Scholar
- Tullio Jappelli. 1990. Who is credit constrained in the US economy?The Quarterly Journal of Economics 105, 1 (1990), 219–234.Google Scholar
- Ji Won Kim and T Makana Chock. 2015. Body image 2.0: Associations between social grooming on Facebook and body image concerns. Computers in Human Behavior 48 (2015), 331–339.Google ScholarDigital Library
- J. Kristensen 2017. Parsimonious data: How a single facebook like predicts voting behavior in multiparty systems. PloS one (2017).Google Scholar
- Juhi Kulshrestha, Marcos Oliveira, Orkut Karacalik, Denis Bonnay, and Claudia Wagner. 2021. Web Routineness and Limits of Predictability: Investigating Demographic Differences Using Web Tracking data. In International Conference on Web and Social Media.Google Scholar
- Steffen Lauritzen and David Spiegelhalter. 1988. Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society 50 (1988), 157–194.Google Scholar
- Caroline Lo, Dan Frankowski, and Jure Leskovec. 2016. Understanding behaviors that lead to purchasing: A case study of pinterest. In ACM SIGKDD.Google Scholar
- Charlotte N Markey and Patrick M Markey. 2005. Relations between body image and dieting behaviors: An examination of gender differences. Sex roles 53, 7-8 (2005), 519–530.Google Scholar
- David Martin, Martin O’neill, Susan Hubbard, and Adrian Palmer. 2008. The role of emotion in explaining consumer satisfaction and future behavioural intention. Journal of Services Marketing 1, 1 (2008).Google Scholar
- Tanushree Mitra, Scott Counts, and James W. Pennebaker. 2016. Understanding Anti-Vaccination Attitudes in Social Media. In International Conference on Web and Social Media.Google Scholar
- Saif M. Mohammad and Peter D. Turney. 2013. Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence(2013), 436–465.Google Scholar
- Changiz Mohiyeddini, Regina Pauli, and Stephanie Bauer. 2009. The role of emotion in bridging the intention–behaviour gap: The case of sports participation. Psychology of Sport and Exercise 10, 2 (2009), 226–234.Google ScholarCross Ref
- Osnat Mokryn, Veronika Bogina, and Tsvi Kuflik. 2019. Will this session end with a purchase? Inferring current purchase intent of anonymous visitors. Electronic Commerce Research and Applications 34 (2019).Google Scholar
- Cristina Ottaviani and Daniela Vandone. 2011. Impulsivity and household indebtedness: Evidence from real life. Journal of economic psychology 32, 5 (2011), 754–761.Google ScholarCross Ref
- B. Rammstedt and O.P. John. 2007. Measuring personality in one minute: A 10-item version of the Big Five Inventory. Journal of Research in Personality 41 (2007), 203–212.Google ScholarCross Ref
- Ramit Sawhney 2021. Towards Emotion- and Time-Aware Classification of Tweets to Assist Human Moderation for Suicide Prevention. In International Conference on Web and Social Media.Google Scholar
- Sanja Scepanovic, Luca Maria Aiello, Ke Zhou, Sagar Joglekar, and Daniele Quercia. 2021. The Healthy States of America: Creating a Health Taxonomy with Social Media. In International Conference on Web and Social Media.Google Scholar
- H Andrew Schwartz 2013. Personality, gender, and age in the language of social media: The open-vocabulary approach. PloS one 8(2013).Google Scholar
- Paschal Sheeran. 2002. Intention—behavior relations: a conceptual and empirical review. European review of social psychology 12 (2002), 1–36.Google Scholar
- Amila Silva, Pei-Chi Lo, and Ee-Peng Lim. 2021. On Predicting Personal Values of Social Media Users using Community-Specific Features and Personal Value Correlation. In International Conference on Web and Social Media.Google Scholar
- Jacopo Staiano, Bruno Lepri, Nadav Aharony, Fabio Pianesi, Nicu Sebe, and Alex Pentland. 2012. Friends don’t lie: inferring personality traits from social network structure. In ACM Conference on Ubiquitous Computing.Google ScholarDigital Library
- Yla Tausczik and James W. Pennebaker. 2010. The psychological meaning of words: LIWC and computerized text analysis methods.Journal of language and social psychology 29, 1 (2010), 24–54.Google Scholar
- M. Kosinski W. Youyou and D. Stillwell. 2015. Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences 112, 4 (2015), 1036–1040.Google ScholarCross Ref
- N.E. Wetherick. 2002. Reclaiming Cognition: The Primacy of Action, Intention and Emotion, eds. R. Nunez & W.J. Freeman. Journal of the British Society for Phenomenology 33, 1(2002), 92–95.Google ScholarCross Ref
- Nan Xi, Di Ma, Marcus Liou, Zachary C Steinert-Threlkeld, Jason Anastasopoulos, and Jungseock Joo. 2020. Understanding the Political Ideology of Legislators from Social Media Images. In International Conference on Web and Social Media.Google Scholar
- F. Zamal, W. Liu, and D. Ruths. 2012. Homophily and latent attribute inference: Inferring latent attributes of twitter users from neighbors,. In International Conference on Web and Social Media.Google Scholar
- Yongzheng Zhang and Marco Pennacchiotti. 2013. Predicting purchase behaviors from social media. In International World Wide Web Conference.Google ScholarDigital Library
Index Terms
- A Network-Based, Multidisciplinary Approach to Intention Inference
Index terms have been assigned to the content through auto-classification.
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