Volume 3, Issue 2
  • ISSN 2452-0063
  • E-ISSN: 2452-0071
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Need for orientation (NFO) has long been accepted as an antecedent to agenda-setting effects. This study assessed whether NFO can go further to explain a specific behavior, why individuals share political news on Facebook. A new method is introduced that combines survey data with users’ Facebook accounts and their actual Facebook posts to reveal the historical news sharing behaviors of 741 U.S. citizens. Computer-assisted content analysis is employed to analyze nearly a million messages for the presence of political news content. Results suggest that a key component found in need for orientation – attention to relevant issues and facts – predicts observed political news sharing on Facebook. Other demographics such as age and gender also predict news sharing behavior. In all, the model employed here significantly predicts news sharing while commonly regarded antecedents to political sharing, including news consumption and political interest, fail to do so.


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  1. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L.
    (2012) The role of social networks in information diffusion. Proceedings of the 21st International Conference on World Wide Web, 519–528. doi:  10.1145/2187836.2187907
    https://doi.org/10.1145/2187836.2187907 [Google Scholar]
  2. Beam, M. A., Hutchens, M. J., & Hmielowski, J. D.
    (2016) Clicking vs. sharing: The relationship between online news behaviors and political knowledge. Computers in Human Behavior, 59, 215–220. doi:  10.1016/j.chb.2016.02.013
    https://doi.org/10.1016/j.chb.2016.02.013 [Google Scholar]
  3. Berger, J., & Milkman, K. L.
    (2012) What makes online content viral?Journal of Marketing Research, 49, 192–205. doi:  10.1509/jmr.10.0353
    https://doi.org/10.1509/jmr.10.0353 [Google Scholar]
  4. Bobkowski, P. S.
    (2015) Sharing the news: Effects of informational utility and opinion leadership on online news sharing. Journalism & Mass Communication Quarterly, 92, 320–345. doi:  10.1177/1077699015573194
    https://doi.org/10.1177/1077699015573194 [Google Scholar]
  5. Breiman, L.
    (2001) Random forests. Machine Learning, 45, 5–32. doi:  10.1023/A:1010933404324
    https://doi.org/10.1023/A:1010933404324 [Google Scholar]
  6. Bressert, E.
    (2012) Scipy and Numpy: An overview for developers. New York, NY: O’Reilly Media, Inc.
    [Google Scholar]
  7. Camaj, L., & Weaver, D. H.
    (2013) Need for orientation and attribute agenda-setting during a US election campaign International Journal of Communication, 7, 1442–1463.
    [Google Scholar]
  8. Caputo, B., Sim, K., Furesjo, F., & Smola, A.
    (2002) Appearance-based object recognition using SVMs: Which kernel should I use?Proceedings of NIPS workshop on Statistical Methods for Computational Experiments in Visual Processing and Computer Vision 2002, 149–158.
    [Google Scholar]
  9. Chernov, G., Valenzuela, S., & McCombs, M.
    (2011) An experimental comparison of two perspectives on the concept of need for orientation in agenda-setting theory. Journalism & Mass Communication Quarterly, 88, 142–155. doi:  10.1177/107769901108800108
    https://doi.org/10.1177/107769901108800108 [Google Scholar]
  10. Fawcett, T.
    (2004) ROC graphs: Notes and practical considerations for researchers. Machine Learning. Retrieved frombinf.gmu.edu/mmasso/ROC101.pdf
    [Google Scholar]
  11. Friedman, J. H.
    (2001) Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 1189–1232. doi:  10.1214/aos/1013203451
    https://doi.org/10.1214/aos/1013203451 [Google Scholar]
  12. Gil de Zúñiga, H., Jung, N., & Valenzuela, S.
    (2012) Social media use for news and individuals’ social capital, civic engagement and political participation. Journal of Computer-Mediated Communication, 17, 319–336. doi:  10.1111/j.1083‑6101.2012.01574.x
    https://doi.org/10.1111/j.1083-6101.2012.01574.x [Google Scholar]
  13. Goyal, A., Bonchi, F., & Lakshmanan, L. V. S.
    (2010) Learning influence probabilities in social networks. Proceedings of the Third ACM International Conference on Web Search and Data Mining, 241–250. doi:  10.1145/1718487.1718518
    https://doi.org/10.1145/1718487.1718518 [Google Scholar]
  14. Hanson, G., & Haridakis, P.
    (2008) YouTube users watching and sharing the news: A uses and gratifications approach. Journal of Electronic Publishing, 11(3). doi:  10.3998/3336451.0011.305
    https://doi.org/10.3998/3336451.0011.305 [Google Scholar]
  15. Hermida, A., Fletcher, F., Korell, D., & Logan, D.
    (2012) Share, like, recommend: Decoding the social media news consumer. Journalism Studies, 13, 815–824. doi:  10.1080/1461670X.2012.664430
    https://doi.org/10.1080/1461670X.2012.664430 [Google Scholar]
  16. Hilbe, J. M.
    (2014) Modeling count data. New York, NY: Cambridge University Press. doi:  10.1017/CBO9781139236065
    https://doi.org/10.1017/CBO9781139236065 [Google Scholar]
  17. Himelboim, I., McCreery, S., & Smith, M.
    (2013) Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on Twitter. Journal of Computer-Mediated Communication, 18, 40–60. 10.1111/jcc4.12001
    https://doi.org/10.1111/jcc4.12001 [Google Scholar]
  18. Holton, A. E., Baek, K., Coddington, M., & Yaschur, C.
    (2014) Seeking and sharing: Motivations for linking on Twitter. Communication Research Reports, 31, 33–40. doi:  10.1080/08824096.2013.843165
    https://doi.org/10.1080/08824096.2013.843165 [Google Scholar]
  19. Horan, T. J.
    (2013) ‘Soft’ versus ‘hard’ news on microblogging networks: Semantic analysis of Twitter produsage. Information, Communication & Society, 16, 43–60. doi:  10.1080/1369118X.2011.649774
    https://doi.org/10.1080/1369118X.2011.649774 [Google Scholar]
  20. Karnowski, V., Kümpel, A. S., Leonhard, L., & Leiner, D. J.
    (2017) From incidental news exposure to news engagement. How perceptions of the news post and news usage patterns influence engagement with news articles encountered on Facebook. Computers in Human Behavior, 76, 42–50. doi:  10.1016/j.chb.2017.06.041
    https://doi.org/10.1016/j.chb.2017.06.041 [Google Scholar]
  21. Kümpel, A. S., Karnowski, V., & Keyling, T.
    (2015) News sharing in social media: A review of current research on news sharing users, content, and networks. Social Media+ Society, 1(2). doi:  10.1177/2056305115610141
    https://doi.org/10.1177/2056305115610141 [Google Scholar]
  22. Lee, C. S., & Ma, L.
    (2012) News sharing in social media: The effect of gratifications and prior experience. Computers in Human Behavior, 28, 331–339. doi:  10.1016/j.chb.2011.10.002
    https://doi.org/10.1016/j.chb.2011.10.002 [Google Scholar]
  23. Matsa, K. E., & Lu, K.
    (2016, September14). 10 facts about the changing digital news landscape. Retrieved fromwww.pewresearch.org/fact-tank/2016/09/14/facts-about-the-changing-digital-news-landscape/
  24. Matthes, J.
    (2006) The need for orientation towards news media: Revising and validating a classic concept. International Journal of Public Opinion Research, 18, 422–444. doi:  10.1093/ijpor/edh118
    https://doi.org/10.1093/ijpor/edh118 [Google Scholar]
  25. (2008) Need for orientation as a predictor of agenda-setting effects: Causal evidence from a two-wave panel study. International Journal of Public Opinion Research, 20, 440–453. doi:  10.1093/ijpor/edn042
    https://doi.org/10.1093/ijpor/edn042 [Google Scholar]
  26. McCombs, M. E., Shaw, D. L., & Weaver, D. H.
    (2014) New directions in agenda-setting theory and research. Mass Communication and Society, 17, 781–802. doi:  10.1080/15205436.2014.964871
    https://doi.org/10.1080/15205436.2014.964871 [Google Scholar]
  27. McCombs, M. E., & Weaver, D. H.
    (1973, April). Voters’ need for orientation and use of mass communication. Paper presented at theAnnual Meeting of the International Communication Association, Montreal, Canada.
    [Google Scholar]
  28. Moy, P., & Murphy, J.
    (2016) Problems and prospects in survey research. Journalism & Mass Communication Quarterly, 93, 16–37. doi: 10.1177/1077/699016631108
    https://doi.org/10.1177/1077/699016631108 [Google Scholar]
  29. Park, N., Kee, K. F., & Valenzuela, S.
    (2009) Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729–733. 10.1089/cpb.2009.0003
    https://doi.org/10.1089/cpb.2009.0003 [Google Scholar]
  30. Pearson, R.
    (2017, April15). Introduction to the DataRobot R package. Retrieved fromhttps://cran.r-project.org/web/packages/datarobot/vignettes/IntroductionToDataRobot.html
  31. Reagan, J., Pinkleton, B., Chen, C., & Aaronson, D.
    (1995) How do technologies relate to the repertoire of information sources?Telematics and Informatics, 12, 21–27. doi:  10.1016/0736‑5853(94)00035‑R
    https://doi.org/10.1016/0736-5853(94)00035-R [Google Scholar]
  32. Ridout, M., Hinde, J., & DeméAtrio, C. G.
    (2001) A score test for testing a zero-inflated Poisson regression model against zero-inflated negative binomial alternatives. Biometrics, 57(1), 219–223. 10.1111/j.0006‑341X.2001.00219.x
    https://doi.org/10.1111/j.0006-341X.2001.00219.x [Google Scholar]
  33. Shaw, D. L., McCombs, M., Weaver, D. H., & Hamm, B. J.
    (1999) Individuals, groups, and agenda melding: A theory of social dissonance. International Journal of Public Opinion Research, 11(1), 2–24. 10.1093/ijpor/11.1.2
    https://doi.org/10.1093/ijpor/11.1.2 [Google Scholar]
  34. Shearer, E. & Matsa, K.
    (2018) News Use Across Social Medial Platforms 2018. Pew Research Center. Retrieved fromwww.journalism.org/2018/09/10/news-use-across-social-media-platforms-2018/
    [Google Scholar]
  35. Silva, S., Anunciação, O., & Lotz, M.
    (2011) A comparison of machine learning methods for the prediction of breast cancer. InC. Pizzuti, M. D. Ritchie, M. Giacobini (Eds.), Lecture Notes in Computer Science: Vol 6623. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (pp.159–170). doi:  10.1007/978‑3‑642‑20389‑3_17
    https://doi.org/10.1007/978-3-642-20389-3_17 [Google Scholar]
  36. Social media fact sheet
    Social media fact sheet (2017, January12). Retrieved fromwww.pewinternet.org/fact-sheet/social-media/
  37. Tourangeau, R.
    (2000) Remembering what happened: Memory errors and survey reports. InA. A. Stone, C. A. Bachrach, J. B. Jobe, H. S. Kurtzman, & V. S. Cain (Eds.), The science of self-report: Implications for research and practice (pp.29–47). Mahwah, NJ: Lawrence Erlbaum.
    [Google Scholar]
  38. Vargo, C., Guo, L. & Amazeen, A.
    (2018) The agenda-setting power of fake News: A big data analysis of the online media landscape from 2014 to 2016. New Media & Society, 20(5) 2028–2049. 10.1177/1461444817712086
    https://doi.org/10.1177/1461444817712086 [Google Scholar]
  39. Weaver, D. H.
    (1977) Political issues and voter need for orientation. InD. L. Shaw and M. E. McCombs (Eds.), The emergence of American political issues: The agenda-setting function of the press (pp.107–120). New York: West Publishing Co.
    [Google Scholar]
  40. (1980) Audience need for orientation and media effects. Communication Research, 7, 361–373. doi:  10.1177/009365028000700305
    https://doi.org/10.1177/009365028000700305 [Google Scholar]
  41. Weeks, B. E., & Holbert, R. L.
    (2013) Predicting dissemination of news content in social media: A focus on reception, friending, and partisanship. Journalism & Mass Communication Quarterly, 90, 212–232. doi:  10.1177/1077699013482906
    https://doi.org/10.1177/1077699013482906 [Google Scholar]
  42. Yuan, E.
    (2011) News consumption across multiple media platforms. Information, Communication, & Society, 14, 998–1016. doi:  10.1080/1369118X.2010.549235
    https://doi.org/10.1080/1369118X.2010.549235 [Google Scholar]
  43. Zhang, Y., & Leung, L.
    (2015) A review of social networking service (SNS) research in communication journals from 2006 to 2011. New Media & Society, 17, 1007–1024. 10.1177/1461444813520477
    https://doi.org/10.1177/1461444813520477 [Google Scholar]
  44. Zhou, Z. H., Wu, J., & Tang, W.
    (2002) Ensembling neural networks: many could be better than all. Artificial intelligence, 137(1–2), 239–263. 10.1016/S0004‑3702(02)00190‑X
    https://doi.org/10.1016/S0004-3702(02)00190-X [Google Scholar]

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