To Share or Not to Share: Investigating Drivers for Sharing Online News Using Automated Machine Learning and Probabilistic Modeling
References
- Kümpel, A. S., V. Karnowski, T. Keyling. News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks. – Social Media + Society, Vol. <bold>1</bold>, 2015, No 2, 2056305115610141.
- Berger, J., K. L. Milkman. What Makes Online Content Viral? – Journal of Marketing Research, Vol. <bold>49</bold>, 2012, No 2, pp. 192-205.
- Hoewe, J., S. Parrott. The Power of Anger: How Emotions Predict Information Seeking and Sharing After a Presidential Election. – Atlantic Journal of Communication, Vol. <bold>27</bold>, 2019, No 4, pp. 272-283.
- Cantwell, O., K. Kushlev. Anxiety Talking: Does Anxiety Predict Sharing Information about COVID-19? 2021.
- Wang, L., S. R. Fussell. More Than a Click: Exploring College Students’ Decision-Making Processes in Online News Sharing. – Proceedings of the ACM on Human-Computer Interaction (GROUP), Vol. <bold>4</bold>, 2020, pp. 1-20.
- Wadbring, I., S. Ödmark. Going Viral: News Sharing and Shared News in Social Media. – OBS-Observatorio, Vol. <bold>10</bold>, 2016, No 4, pp. 132-149.
- Thompson, N., X. Wang, P. Daya. Determinants of News Sharing Behavior on Social Media. – Journal of Computer Information Systems, 2019.
- Bobkowski, P. S. Sharing the News: Effects of Informational Utility and Opinion Leadership on Online News Sharing. – Journalism & Mass Communication Quarterly, Vol. <bold>92</bold>, 2015, No 2, pp. 320-345.
- Ihm, J., E. M. Kim. The Hidden Side of News Diffusion: Understanding Online News Sharing as an Interpersonal Behavior. – New Media & Society, Vol. <bold>20</bold>, 2018, No 11, pp. 4346-4365.
- Beam, M. A., M. J. Hutchens, J. D. Hmielowski. Clicking vs Sharing: The Relationship between Online News Behaviors and Political Knowledge. – Computers in Human Behavior, Vol. <bold>59</bold>, 2016, pp. 215-220.
- Shi, Z., H. Rui, A. B. Whinston. Content Sharing in a Social Broadcasting Environment: Evidence from Twitter. – MIS Quarterly, Vol. <bold>38</bold>, 2014, No 1 pp. 123-142.
- Sharma, A., D. Cosley. Studying and Modeling the Connection Between People’s Preferences and Content Sharing. – In: Proc. of 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 2015, pp. 1246-1257.
- Scholz, C., M. Jovanova, E. C. Baek, E. B. Falk. Media Content Sharing as a Value-Based Decision. – Current Opinion in Psychology, Vol. <bold>31</bold>, 2020, pp. 83-88.
- Hsiao, C. C. Understanding Content Sharing on the Internet: A Test of a Cognitive-Affective-Conative Model. – Online Information Review, 2020.
- Su, M. H., J. Liu, D. M. McLeod. Pathways to News Sharing: Issue Frame Perceptions and the Likelihood of Sharing. – Computers in Human Behavior, Vol. <bold>91</bold>, 2019, pp. 201-210.
- Bhagat, S., D. J. Kim. Examining Users’ News Sharing Behavior on Social Media: Role of Perception of Online Civic Engagement and Dual Social Influences. – Behavior & Information Technology, 2022, pp. 1-22.
- Trilling, D., J. Kulshrestha, C. de Vreese, D. Halagiera, J. Jakubowski, J. Moeller, C. Vaccari. Is Sharing Just a Function of Viewing? – Predictors of Sharing Political and Non-Political News on Facebook. 2022.
- Prado-Romero, M. A., A. Coto-Santiesteban, A. Celi, G. Stilo. A Time-Sensitive Model to Predict Topic Popularity in News Providers. – Intelligent Data Analysis, Vol. <bold>24</bold>, 2020, No S1, pp. 123-140.
- Meghawat, M., S. Yadav, D. Mahata, Y. Yin, R. R. Shah, R. Zimmermann. A Multimodal Approach to Predict Social Media Popularity. – In: IEEE Conference on Multimedia Information Processing and Retrieval (MIPR’18), IEEE, 2018, pp. 190-195.
- Manzoor, S. I., J. Singla. Fake News Detection Using Machine Learning Approaches: A Systematic Review. – In: 3rd IEEE International Conference on Trends in Electronics and Informatics (ICOEI’19), 2019, pp. 230-234.
- Gerunov, A. Performance of 109 Machine Learning Algorithms across Five Forecasting Tasks: Employee Behavior Modeling, Online Communication, House Pricing, IT Support, and Demand Planning. – Economic Studies, Vol. <bold>31</bold>, 2022, No 2, pp. 15-43.
- Fernandes, K., P. Vinagre, P. Cortez. A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News. – In: Portuguese Conference on Artificial Intelligence. Cham., Springer, 2015, pp. 535-546.
- Gerunov, A. Automated Approaches for Operational Risk Management. Sofia University “St. Kliment Ohridski”, 2020 (in Bulgarian).
- Blei, D. M., A. Y. Ng, M. I. Jordan. Latent Dirichlet Allocation. – Journal of Machine Learning Research, Vol. <bold>3</bold>, January 2003, pp. 993-1022.
- Yue, L., W. Chen, X. Li, W. Zuo, M. Yin. A Survey of Sentiment Analysis in Social Media. – Knowledge and Information Systems, Vol. <bold>60</bold>, 2019, pp. 617-663.
- Ratkiewicz, J., S. Fortunato, A. Flammini, F. Menczer, A. Vespignani. Characterizing and Modeling the Dynamics of Online Popularity. – Physical Review Letters, Vol. <bold>105</bold>, 2010, No 15, 158701.
- Fernández-Delgado, M., E. Cernadas, S. Barro, D. Amorim. Do We Need Hundreds of Classifiers to Solve Real-World Classification Problems? – The Journal of Machine Learning Research, Vol. <bold>15</bold>, 2014, No 1, pp. 3133-3181.
- He, X., K. Zhao, X. Chu. AutoML: A Survey of the State-of-the-Art. – Knowledge-Based Systems, Vol. <bold>212</bold>, 2021, 106622.
- Yao, Q., M. Wang, Y. Chen, W. Dai, Y. F. Li, W. W. Tu,. Y. Yu. Taking Human out of Learning Applications: A Survey on Automated Machine Learning. – arXiv preprint arXiv:1810.13306, 2018.
- Hutter, F., L. Kotthoff, J. Vanschoren. Automated Machine Learning: Methods, Systems, Challenges. – In: Springer Nature Link, 2019, p. 219.
- LeDell, E., S. Poirier. H2O Autonomy: Scalable Automatic Machine Learning. – In: Proc. of AutoML Workshop at ICML, Vol. <bold>2020</bold>, July 2020.
- R. G. Cowell, Ed. Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks. Springer Science & Business Media, 2006.
- Korb, K., A. E. Nicholson. Bayesian Artificial Intelligence. 2nd Edition, Chapman & Hall/CRC, 2010.
- Højsgaard, S. Graphical Independence Networks with the gRain Package for R. – Journal of Statistical Software, Vol. <bold>46</bold>, 2012, No 10, pp. 1-26.
- Wang, C. H., H. Y. Cheng, Y. T. Deng. Using Bayesian Belief Network and Time-Series Model to Conduct Prescriptive and Predictive Analytics for Computer Industries. – Computers & Industrial Engineering, Vol. <bold>115</bold>, 2018, pp. 486-494.
- Scutari, M., C. E. Graafland, J. M. Gutiérrez. Who Learns Better Bayesian Network Structures: Constraint-Based, Score-Based, or Hybrid Algorithms? – In: Proc. of International Conference on Probabilistic Graphical Models PMLR, August 2018, pp. 416-427.
- Gámez, J. A., J. L. Mateo, J. M. Puerta. Learning Bayesian Networks by Hill Climbing: Efficient Methods Based on Progressive Restriction of the Neighborhood. – Data Mining and Knowledge Discovery, Vol. <bold>22</bold>, 2011, No 1, pp. 106-148.
- Gerunov, A. Networks of Risk. – In: Risk Analysis for the Digital Age. Cham, Springer International Publishing, 2022, pp. 115-156.
- LeDell, E., S. Poirier. H2O Autonomy: Scalable Automatic Machine Learning. – In: Proc. of AutoML Workshop at ICML, Vol. <bold>2020</bold>, July 2020, San Diego, CA, USA, (ICML’20).
- Watson, J., S. van der Linden, M. Watson, D. Stillwell. Negative Online News Articles Are Shared More on Social Media. – Scientific Reports, Vol. <bold>14</bold>, 2024, No 1, 21592.
- van der Meer, T. G., A. Brosius. Credibility and Shareworthiness of Negative News. – Journalism, Vol. <bold>25</bold>, 2024, No 1, pp. 61-80.
- Mathews, N., V. Bélair-Gagnon, S. C. Lewis. News is “Toxic”: Exploring the Non-Sharing of News Online. – New Media & Society, Vol. <bold>26</bold>, 2024, No 8, pp. 4629-4646.
Language: English
Page range: 36 - 54
Submitted on: Dec 5, 2024
Accepted on: Feb 21, 2025
Published on: Mar 21, 2025
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
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© 2025 Anton A. Gerunov, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.