Using emotions to predict user interest areas in online social networks

Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015

Volumn , Issue , 2015, Pages

  Lewenberg, Yoad ^a   Bachrach, Yoram ^b   Volkova, Svitlana ^c  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

^b MICROSOFT RESEARCH   (United Kingdom)

^c Johns Hopkins University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; ARTS COMPUTING; BEHAVIORAL RESEARCH; LEARNING SYSTEMS;

AMAZON'S MECHANICAL TURKS; DEGREE OF INTERESTS; ON-LINE SOCIAL NETWORKS; TRAINING MACHINES; USER INTERESTS;

SOCIAL NETWORKING (ONLINE);

EID: 84962850051     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DSAA.2015.7344887     Document Type: Conference Paper

References (76)

1. E. Qualman, Socialnomics: How social media transforms the way we live and do business. John Wiley & Sons, 2010.
2. T. L. Tuten, Advertising 2.0: social media marketing in a web 2.0 world. Greenwood Publishing Group, 2008.
3. W. G. Mangold and D. J. Faulds, "Social media: The new hybrid element of the promotion mix," Business horizons, vol. 52, no. 4, pp. 357-365, 2009.
4. D. L. Hoffman and M. Fodor, "Can you measure the roi of your social media marketing," MIT Sloan Management Review, vol. 52, no. 1, pp. 41-49, 2010.
5. J. Golbeck, C. Robles, M. Edmondson, and K. Turner, "Predicting personality from Twitter," in Proceedings of SocialCom/PASSAT, 2011.
6. Y. Bachrach, M. Kosinski, T. Graepel, P. Kohli, and D. Stillwell, "Personality and patterns of Facebook usage," in Proceedings of WebSci, 2012, pp. 24-32.
7. M. Kosinski, D. Stillwell, and T. Graepel, "Private traits and attributes are predictable from digital records of human behavior," National Academy of Sciences, 2013.
8. J. Bollen, H. Mao, and X. Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, vol. 2, no. 1, pp. 1-8, 2011.
9. S. Mohammad and S. Kiritchenko, "Using nuances of emotion to identify personality," in Proceedings of ICWSM, 2013.
10. S. Volkova, T. Wilson, and D. Yarowsky, "Exploring demographic language variations to improve sentiment analysis in social media," in Proceedings of EMNLP, 2013.
11. Y. Yang, J. Jia, S. Zhang, B. Wu, J. Li, and J. Tang, "How do your friends on social media disclose your emotions?" in Proc. AAAI, vol. 14, 2014, pp. 1-7.
12. A. M. Kaplan and M. Haenlein, "Users of the world, unite! the challenges and opportunities of social media," Business horizons, vol. 53, no. 1, pp. 59-68, 2010.
13. M. Zappavigna, Discourse of Twitter and social media: How we use language to create affiliation on the web. A&C Black, 2012.
14. H. A. Schwartz, J. C. Eichstaedt, M. L. Kern, L. Dziurzynski, S. M. Ramones, M. Agrawal, A. Shah, M. Kosinski, D. Stillwell, M. E. Seligman et al., "Personality, gender, and age in the language of social media: The open-vocabulary approach," PloS one, vol. 8, no. 9, P. e73791, 2013.
15. D. Bamman, J. Eisenstein, and T. Schnoebelen, "Gender identity and lexical variation in social media," Journal of Sociolinguistics, vol. 18, no. 2, pp. 135-160, 2014.
16. E. K. Clemons, S. Barnett, and A. Appadurai, "The future of advertising and the value of social network websites: some preliminary examinations," in Proceedings of the ninth international conference on Electronic commerce. ACM, 2007, pp. 267-276.
17. M. Trusov, R. E. Bucklin, and K. Pauwels, "Effects of word-of-mouth versus traditional marketing: findings from an internet social networking site," Journal of marketing, vol. 73, no. 5, pp. 90-102, 2009.
18. D. G. Taylor, J. E. Lewin, and D. Strutton, "Friends, fans, and followers: do ads work on social networks?" Business Faculty Publications, 2011.
19. W.-S. Yang, J.-B. Dia, H.-C. Cheng, and H.-T. Lin, "Mining social networks for targeted advertising," in System Sciences, 2006. HICSS'06. Proceedings of the 39th Annual Hawaii International Conference on, vol. 6. IEEE, 2006, pp. 137a-137a.
20. C. E. Tucker, "Social networks, personalized advertising, and privacy controls," Journal of Marketing Research, vol. 51, no. 5, pp. 546-562, 2014.
21. P. Resnick and H. R. Varian, "Recommender systems," Communications of the ACM, vol. 40, no. 3, pp. 56-58, 1997.
22. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," in Proceedings of the 10th international conference on World Wide Web. ACM, 2001, pp. 285-295.
23. -, "Application of dimensionality reduction in recommender system-a case study," DTIC Document, Tech. Rep., 2000.
24. Y. Koren, R. Bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Computer, no. 8, pp. 30-37, 2009.
25. Y. Bachrach, R. Herbrich, and E. Porat, "Sketching algorithms for approximating rank correlations in collaborative filtering systems," in SPIRE, 2009.
26. Y. Bachrach, E. Porat, and J. S. Rosenschein, "Sketching techniques for collaborative filtering," in IJCAI, Pasadena, California, July 2009.
27. Y. Bachrach and R. Herbrich, "Fingerprinting ratings for collaborative filteringtheoretical and empirical analysis," in SPIRE. Springer, 2010, pp. 25-36.
28. P. Li and C. König, "b-bit minwise hashing," in Proceedings of the 19th international conference on World wide web. ACM, 2010, pp. 671-680.
29. A. Dasgupta, R. Kumar, and T. Sarlós, "Fast locality-sensitive hashing," in KDD. ACM, 2011, pp. 1073-1081.
30. Y. Bachrach and E. Porat, "Sketching for big data recommender systems using fast pseudo-random fingerprints," in ICALP, 2013, pp. 459-471.
31. Y. Bachrach, Y. Finkelstein, R. Gilad-Bachrach, L. Katzir, N. Koenig-stein, N. Nice, and U. Paquet, "Speeding up the xbox recommender system using a euclidean transformation for inner-product spaces," in RecSys, 2014.
32. Y. Bachrach and E. Porat, "Fingerprints for highly similar streams," Information and Computation, 2015.
33. D. B. Montgomery and A. J. Silk, "Clusters of consumer interests and opinion leaders' spheres of influence," Journal of Marketing Research, pp. 317-321, 1971.
34. S.-S. Weng and M.-J. Liu, "Feature-based recommendations for one-to-one marketing," Expert Systems with Applications, vol. 26, no. 4, pp. 493-508, 2004.
35. Y. Bachrach, S. Ceppi, I. A. Kash, P. Key, F. Radlinski, E. Porat, M. Armstrong, and V. Sharma, "Building a personalized tourist attraction recommender system using crowdsourcing," in AAMAS, 2014, pp. 1631-1632.
36. P.-T. Chen, J. Z. Cheng, Y.-W. Yu, and P.-H. Ju, "Mobile advertising setting analysis and its strategic implications," Technology in Society, vol. 39, pp. 129-141, 2014.
37. Y. R. Tausczik and J. W. Pennebaker, "The psychological meaning of words: Liwc and computerized text analysis methods," Journal of language and social psychology, vol. 29, no. 1, pp. 24-54, 2010.
38. B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and trends in information retrieval, vol. 2, no. 1-2, pp. 1-135, 2008.
39. C. Strapparava and R. Mihalcea, "Learning to identify emotions in text," in Proceedings of the 2008 ACM symposium on Applied computing. ACM, 2008, pp. 1556-1560.
40. J. T. Hancock, C. Landrigan, and C. Silver, "Expressing emotion in text-based communication," in Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 2007, pp. 929-932.
41. M. Thelwall, D. Wilkinson, and S. Uppal, "Data mining emotion in social network communication: Gender differences in myspace," Journal of the American Society for Information Science and Technology, vol. 61, no. 1, pp. 190-199, 2010.
42. S. Stieglitz and L. Dang-Xuan, "Emotions and information diffusion in social mediasentiment of microblogs and sharing behavior," Journal of Management Information Systems, vol. 29, no. 4, pp. 217-248, 2013.
43. S. M. Mohammad and S. Kiritchenko, "Using hashtags to capture fine emotion categories from tweets," Computational Intelligence, 2014.
44. P. Ekman, "An argument for basic emotions," Cognition & Emotion, vol. 6, no. 3-4, pp. 169-200, May 1992. [Online]. Available: http://dx.doi.org/10.1080/02699939208411068
45. W. Wang, L. Chen, K. Thirunarayan, and A. P. Sheth, "Harnessing Twitter" big data" for automatic emotion identification," in Proceedings of SocialCom, 2012, pp. 587-592.
46. K. Roberts, M. A. Roach, J. Johnson, J. Guthrie, and S. M. Harabagiu, "Empatweet: Annotating and detecting emotions on Twitter." in Proceedings of LREC, 2012.
47. S. Kim, J. Bak, and A. H. Oh, "Do you feel what I feel? Social aspects of emotions in Twitter conversations." in Proceedings of ICWSM, 2012.
48. D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta, "Classifying latent user attributes in Twitter," in Proceedings of SMUC, 2010, pp. 37-44.
49. J. D. Burger, J. Henderson, G. Kim, and G. Zarrella, "Discriminating gender on Twitter," in Proceedings of EMNLP, 2011, pp. 1301-1309.
50. B. Van Durme, "Streaming analysis of discourse participants," in Proceedings of EMNLP, 2012, pp. 48-58.
51. M. Ciot, M. Sonderegger, and D. Ruths, "Gender inference of Twitter users in non-english contexts." in Proceedings of EMNLP, 2013, pp. 1136-1145.
52. S. Mohammad and T. Yang, "Tracking sentiment in mail: How genders differ on emotional axes," in Proceedings of WASSA, 2011, pp. 70-79.
53. D. Nguyen, R. Gravel, D. Trieschnigg, and T. Meder, ""How old do you think I am?" A study of language and age in Twitter," in Proceedings of ICWSM, 2013, pp. 439-448.
54. Y. Bachrach, T. Graepel, P. Kohli, M. Kosinski, and D. Stillwell, "Your digital image: factors behind demographic and psychometric predictions from social network profiles," in AAMAS, 2014, pp. 1649-1650.
55. M. Pennacchiotti and A. M. Popescu, "A machine learning approach to Twitter user classification," in Proceedings of ICWSM, 2011, pp. 281-288.
56. R. Cohen and D. Ruths, "Classifying Political Orientation on Twitter: It's Not Easy!" in Proceedings of ICWSM, 2013.
57. T. A. Mooradian and J. M. Olver, "Shopping motives and the five factor model: An integration and preliminary study," Psychological Reports, vol. 78, no. 2, pp. 579-592, 1996.
58. J.-H. Huang and Y.-C. Yang, "The relationship between personality traits and online shopping motivations," Social Behavior and Personality: an international journal, vol. 38, no. 5, pp. 673-679, 2010.
59. M. Kosinski, D. Stillwell, P. Kohli, Y. Bachrach, and T. Graepel, "Personality and website choice," 2012.
60. M. Kosinski, Y. Bachrach, P. Kohli, D. Stillwell, and T. Graepel, "Manifestations of user personality in website choice and behaviour on online social networks," Machine Learning, vol. 95, no. 3, pp. 357-380, 2014.
61. M. D. Conover, J. Ratkiewicz, M. Francisco, B. Gonc, A. Flammini, and F. Menczer, "Political polarization on Twitter," in Proceedings of ICWSM, 2011, pp. 89-96.
62. F. A. Zamal, W. Liu, and D. Ruths, "Homophily and latent attribute inference: Inferring latent attributes of Twitter users from neighbors," in Proceedings of ICWSM, 2012, pp. 387-390.
63. S. Volkova, G. Coppersmith, and B. Van Durme, "Inferring user political preferences from streaming communications," in Proceedings of ACL, 2014, pp. 186-196.
64. S. Volkova, B. Van Durme, D. Yarowsky, and Y. Bachrach, "Social media predictive analytics.
65. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vander-plas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duch-esnay, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
66. N. A. Smith, "Log-linear models," 2004.
67. B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques," in Proceedings of EMNLP, 2002, pp. 79-86.
68. H. Zheng, D. Li, and W. Hou, "Task design, motivation, and participation in crowdsourcing contests," International Journal of Electronic Commerce, vol. 15, no. 4, pp. 57-88, 2011.
69. X. A. Gao, Y. Bachrach, P. Key, and T. Graepel, "Quality expectation-variance tradeoffs in crowdsourcing contests." in AAAI, 2012.
70. J. Witkowski, Y. Bachrach, P. Key, and D. C. Parkes, "Dwelling on the negative: Incentivizing effort in peer prediction," in HCOMP, 2013.
71. P. Welinder, S. Branson, P. Perona, and S. J. Belongie, "The multidimensional wisdom of crowds," in NIPS, 2010, pp. 2424-2432.
72. Y. Bachrach, T. Graepel, T. Minka, and J. Guiver, "How to grade a test without knowing the answers-a bayesian graphical model for adaptive crowdsourcing and aptitude testing," ICML, 2012.
73. M. Salek, Y. Bachrach, and P. Key, "Hotspotting-a probabilistic graphical model for image object localization through crowdsourcing." in AAAI, 2013.
74. B. Shalem, Y. Bachrach, J. Guiver, and C. M. Bishop, "Students, teachers, exams and moocs: Predicting and optimizing attainment in web-based education using a probabilistic graphical model," in ECML/PKDD. Springer, 2014, pp. 82-97.
75. Y. Bachrach, T. Graepel, G. Kasneci, M. Kosinski, and J. Van Gael, "Crowd iq: aggregating opinions to boost performance," in AAMAS, 2012, pp. 535-542.
76. M. Kosinski, Y. Bachrach, G. Kasneci, J. Van-Gael, and T. Graepel, "Crowd iq: Measuring the intelligence of crowdsourcing platforms," in WebSci. ACM, 2012, pp. 151-160.