References
- Adam, K. (2019, October 22). Why didn’t Greta Thunberg win the Nobel Peace Prize? The Washington Post.
https://www.washingtonpost.com/world/europe/why-didnt-greta-thunberg-win-the-nobel-peace-prize/2019/10/11/e84e6efc-eba4-11e9-9306-47cb0324fd44_story.html . - Asikainen, A., Iñiguez, G., Ureña-Carrión, J., Kaski, K., & Kivelä, M. (2020). Cumulative effects of triadic closure and homophily in social networks. Science Advances, 6(19).
https://doi.org/10.1126/sciadv.aax7310 - Babyak, M. A. (2004). What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic medicine, 66(3), 411–421.
https://doi.org/10.1097/01.psy.0000127692.23278.a9 - Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221.
https://doi.org/10.1073/pnas.1804840115 - Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542.
https://doi.org/10.1177/0956797615594620 - Barbieri, N., Bonchi, F., & Manco, G. (2013). Topic-aware social influence propagation models. Knowledge and Information Systems, 37(3), 555–584.
https://doi.org/10.1007/s10115-013-0646-6 - Barrat, A., Barthelemy, M., & Vespignani, A. (2008). Dynamical processes on complex networks. Cambridge University Press.
- Baumann, F., Lorenz-Spreen, P., Sokolov, I. M., & Starnini, M. (2020). Modeling echo chambers and polarization dynamics in social networks. Physical Review Letters, 124(4), 048301.
https://doi.org/10.1103/PhysRevLett.124.048301 - Bench, S. W., & Lench, H. C. (2013). On the function of boredom. Behavioral Sciences, 3(3), 459–472.
https://doi.org/10.3390/bs3030459 - Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.
https://doi.org/10.1509/jmr.10.0353 - Birch, S. (2020). Political polarization and environmental attitudes: A cross-national analysis. Environmental Politics, 29(4), 697–718.
https://doi.org/10.1080/09644016.2019.1673997 - Carrington, D. (2019, March 14). Greta Thunberg nominated for Nobel Peace Prize. The Guardian.
https://www.theguardian.com/world/2019/mar/14/greta-thunberg-nominated-nobel-peace-prize - Chen, T. H. Y., Salloum, A., Gronow, A., Ylä-Anttila, T., & Kivelä, M. (2020, July 6). Polarization of climate politics results from partisan sorting: Evidence from Finnish Twittersphere. arXiv preprint arXiv:2007.02706.
https://doi.org/10.31235/osf.io/49qgu - Cody, E. M., Reagan, A. J., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2015). Climate change sentiment on twitter: An unsolicited public opinion poll. PloS one, 10(8), 1–18.
https://doi.org/10.1371/journal.pone.0136092 - Dahal, B., Kumar, S. A., & Li, Z. (2019). Topic modeling and sentiment analysis of global climate change tweets. Social Network Analysis and Mining, 9(24).
https://doi.org/10.1007/s13278-019-0568-8 - Dahlgren, P. M. (2021). A critical review of filter bubbles and a comparison with selective exposure. Nordicom Review, 42(1), 15–33.
https://doi.org/10.2478/nor-2021-0002 - Fariss, C. J., & Jones, Z. M. (2018). Enhancing validity in observational settings when replication is not possible. Political Science Research and Methods, 6(2), 365–380.
https://doi.org/10.1017/psrm.2017.5 - Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018). Quantifying controversy on social media. ACM Transactions on Social Computing, 1(1), 1–27.
https://doi.org/10.1145/3140565 - Guerini, M., Strapparava, C., & Ozbal, G. (2011). Exploring text virality in social networks. Proceedings of the International AAAI Conference on Web and Social Media, 5(1).
- Hansen, L. K., Arvidsson, A., Nielsen, F. Å., Colleoni, E., & Etter, M. (2011, June 28–30). Good friends, bad news-affect and virality in twitter. In J. J. J. H. Park, L. T. Yang, & C. Lee (Eds.), Future information technology: 6th International Conference on Future Information Technology (pp. 34–43). Springer.
https://doi.org/10.1007/978-3-642-22309-9 - Hanteer, O., & Rossi, L. (2019). An innovative way to model twitter topic-driven interactions using multiplex networks. Frontiers in Big Data, 2(9).
https://doi.org/10.3389/fdata.2019.00009 - Hawkins, D. M. (2004). The problem of overfitting. Journal of Chemical Information and Computer Sciences, 44(1), 1–12.
https://doi.org/10.1021/ci0342472 - Jang, S. M., & Hart, P. S. (2015). Polarized frames on “climate change” and “global warming” across countries and states: Evidence from twitter Big Data. Global Environmental Change, 32, 11–17.
https://doi.org/10.1016/j.gloenvcha.2015.02.010 - Jenders, M., Kasneci, G., & Naumann, F. (2013). Analyzing and predicting viral tweets. Proceedings of the 22nd international conference on world wide web (pp. 657–664).
https://doi.org/10.1145/2487788.2488017 - Karypis, G. & Kumar, V. (1998). A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on scientific Computing, 20(1), 359–392.
https://dl.acm.org/doi/10.5555/305219.305248 - Kempe, D., Kleinberg, J., & Tardos, É. (2003, August). Maximizing the spread of influence through a social network. Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 137–146).
- Kirilenko, A. P. & Stepchenkova, S. O. (2014). Public microblogging on climate change: One year of twitter worldwide. Global Environmental Change, 26, 171–182.
https://doi.org/10.1016/j.gloenvcha.2014.02.008 - McCright, A. M., & Dunlap, R. E. (2011). The politicization of climate change and polarization in the American public's views of global warming, 2001–2010. The Sociological Quarterly, 52(2), 155–194.
https://doi.org/10.1111/j.1533-8525.2011.01198.x - McNeish, D. M. (2015). Using lasso for predictor selection and to assuage overfitting: A method long overlooked in behavioral sciences. Multivariate Behavioral Research, 50(5), 471–484.
https://doi.org/10.1080/00273171.2015.1036965 - Muddiman, A., McGregor, S. C., & Stroud, N. J. (2019). (Re)claiming our expertise: Parsing large text corpora with manually validated and organic dictionaries. Political Communication, 36(2), 214–226.
https://doi.org/10.1080/10584609.2018.1517843 - Munger, K. (2019). The limited value of non-replicable field experiments in contexts with low temporal validity. Social Media + Society, 5(3), 1–4.
https://doi.org/10.1177/2056305119859294 - Musco, C., Musco, C., & Tsourakakis, C. E. (2018). Minimizing polarization and disagreement in social networks. Proceedings of the 2018 World Wide Web Conference (pp. 369–378).
- Nagarajan, M., Purohit, H., & Sheth, A. (2010). A qualitative examination of topical tweet and retweet practices. Fourth International AAAI Conference on Weblogs and Social Media. Citeseer.
- Nelimarkka, M., Laaksonen, S.-M., & Semaan, B. (2018). Social media is polarized, social media is polarized: Towards a new design agenda for mitigating polarization. Proceedings of the 2018 Designing Interactive Systems Conference (pp. 957–970).
- Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin UK.
- Pearce, W., Holmberg, K., Hellsten, I., & Nerlich, B. (2014). Climate change on Twitter: Topics, communities and conversations about the 2013 IPCC working group 1 report. PloS one, 9(4), e94785.
https://doi.org/10.1371/journal.pone.0094785 - Saito, K., Nakano, R., & Kimura, M. (2008). Prediction of information diffusion probabilities for independent cascade model. International conference on knowledge-based and intelligent information and engineering systems (pp. 67–75). Springer.
- Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217–248.
https://doi.org/10.2753/MIS0742-1222290408 - Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. 2010 IEEE Second International Conference on Social Computing (pp. 177–184). IEEE.
- Tyagi, A., Uyheng, J., & Carley, K. M. (2020). Affective polarization in online climate change discourse on twitter. arXiv preprint arXiv:2008.13051.
- Weng, L., Menczer, F., & Ahn, Y.-Y. (2013). Virality prediction and community structure in social networks. Scientific reports, 3, 2522.
https://doi.org/10.1038/srep02522 - Williams, H. T., McMurray, J. R., Kurz, T., & Lambert, F. H. (2015). Network analysis reveals open forums and echo chambers in social media discussions of climate change. Global environmental change, 32, 126–138.
https://doi.org/10.1016/j.gloenvcha.2015.03.006 - Yuan, M., & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 68(1), 49–67.
https://doi.org/10.1111/j.1467-9868.2005.00532.x - Zuckerman, M. (2010). Sensation seeking. The Corsini encyclopedia of psychology, 1–4.