Negativity drives online news consumption.

Publisher: Springer Nature Publishing Country of Publication: England NLM ID: 101697750 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2397-3374 (Electronic) Linking ISSN: 23973374 NLM ISO Abbreviation: Nat Hum Behav Subsets: MEDLINE

Original Publication: [London] : Springer Nature Publishing, [2017]-

Internet* ; Information Seeking Behavior* ; Emotions* ; Mass Media*; Humans ; Causality

Online media is important for society in informing and shaping opinions, hence raising the question of what drives online news consumption. Here we analyse the causal effect of negative and emotional words on news consumption using a large online dataset of viral news stories. Specifically, we conducted our analyses using a series of randomized controlled trials (N = 22,743). Our dataset comprises ~105,000 different variations of news stories from Upworthy.com that generated ∼5.7 million clicks across more than 370 million overall impressions. Although positive words were slightly more prevalent than negative words, we found that negative words in news headlines increased consumption rates (and positive words decreased consumption rates). For a headline of average length, each additional negative word increased the click-through rate by 2.3%. Our results contribute to a better understanding of why users engage with online media.
(© 2023. The Author(s).)

Pooley, E. Grins, gore and videotape: the trouble with local TV news. N. Y. Mag. 22, 36–44 (1989).
Americans almost equally prefer to get local news online or on TV Set. Pew Research Center (March 26, 2019); https://www.pewresearch.org/journalism/2019/03/26/nearly-as-many-americans-prefer-to-get-their-local-news-online-as-prefer-the-tv-set/.
Olmstead, K., Mitchell, A. & Rosenstiel, T. Navigating News Online: Where People Go, How They Get There and What Lures Them Away (Pew Research Center’s Project for Excellence in Journalism, 2011).
Simon, H. A. in Computers, Communications, and the Public Interest (ed. Greenberger, M.) 38–72 (The Johns Hopkins Press, 1971).
Flaxman, S., Goel, S. & Rao, J. M. Filter bubbles, echo chambers, and online news consumption. Public Opin. Q. 80, 298–320 (2016). (PMID: 10.1093/poq/nfw006)
Schmidt, A. L. et al. Anatomy of news consumption on Facebook. Proc. Natl Acad. Sci. USA 114, 3035–3039 (2017). (PMID: 28265082537335410.1073/pnas.1617052114)
Bakshy, E., Messing, S. & Adamic, L. A. Exposure to ideologically diverse news and opinion on Facebook. Science 348, 1130–1132 (2015). (PMID: 2595382010.1126/science.aaa1160)
Allen, J., Howland, B., Mobius, M., Rothschild, D. & Watts, D. J. Evaluating the fake news problem at the scale of the information ecosystem. Sci. Adv. 6, eaay3539 (2020). (PMID: 32284969712495410.1126/sciadv.aay3539)
Yang, T., Majó-Vázquez, S., Nielsen, R. K. & González-Bailón, S. Exposure to news grows less fragmented with an increase in mobile access. Proc. Natl Acad. Sci. USA 117, 28678–28683 (2020). (PMID: 33127755768238210.1073/pnas.2006089117)
Godes, D. & Mayzlin, D. Using online conversations to study word-of-mouth communication. Mark. Sci. 23, 545–560 (2004). (PMID: 10.1287/mksc.1040.0071)
Berger, J. & Schwartz, E. M. What drives immediate and ongoing word of mouth? J. Mark. Res. 48, 869–880 (2011). (PMID: 10.1509/jmkr.48.5.869)
Antweiler, W. & Frank, M. Z. Is all that talk just noise? The information content of Internet stock message boards. J. Finance 59, 1259–1294 (2004). (PMID: 10.1111/j.1540-6261.2004.00662.x)
Rapoza, K. Can ‘fake news’ impact the stock market? Forbes https://www.forbes.com/sites/kenrapoza/2017/02/26/can-fake-news-impact-the-stock-market/?sh=1742a00e2fac (2017).
Bollen, J., Mao, H. & Zeng, X. Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011). (PMID: 10.1016/j.jocs.2010.12.007)
Garfin, D. R., Silver, R. C. & Holman, E. A. The novel coronavirus (COVID-2019) outbreak: amplification of public health consequences by media exposure. Health Psychol. 39, 355–357 (2020). (PMID: 32202824773565910.1037/hea0000875)
Aral, S. & Eckles, D. Protecting elections from social media manipulation. Science 365, 858–861 (2019). (PMID: 3146720610.1126/science.aaw8243)
Bond, R. M. et al. A 61-million-person experiment in social influence and political mobilization. Nature 489, 295–298 (2012). (PMID: 2297230010.1038/nature11421)
Jones, J. J., Bond, R. M., Bakshy, E., Eckles, D. & Fowler, J. H. Social influence and political mobilization: further evidence from a randomized experiment in the 2012 U.S. presidential election. PLoS ONE 12, e0173851 (2017). (PMID: 28445476540591610.1371/journal.pone.0173851)
Levy, R. Social media, news consumption, and polarization: evidence from a field experiment. Am. Econ. Rev. 111, 831–870 (2020). (PMID: 10.1257/aer.20191777)
Klein, E. Why We’re Polarized (Avid Reader Press, Simon & Schuster, 2020).
Crockett, M. J. Moral outrage in the digital age. Nat. Hum. Behav. 1, 769–771 (2017). (PMID: 3102411710.1038/s41562-017-0213-3)
Vosoughi, S., Roy, D. & Aral, S. The spread of true and false news online. Science 359, 1146–1151 (2018). (PMID: 2959004510.1126/science.aap9559)
Sanders, S. Upworthy was one of the hottest sites ever. You won’t believe what happened next. NPR https://www.npr.org/sections/alltechconsidered/2017/06/20/533529538/upworthy-was-one-of-the-hottest-sites-ever-you-wont-believe-what-happened-next (2017).
Baumeister, R. F., Bratslavsky, E., Finkenauer, C. & Vohs, K. D. Bad is stronger than good. Rev. Gen. Psychol. 5, 323–370 (2001). (PMID: 10.1037/1089-2680.5.4.323)
Rozin, P. & Royzman, E. B. Negativity bias, negativity dominance, and contagion. Pers. Soc. Psychol. Rev. 5, 296–320 (2001). (PMID: 10.1207/S15327957PSPR0504_2)
Carver, L. J. & Vaccaro, B. G. 12-month-old infants allocate increased neural resources to stimuli associated with negative adult emotion. Dev. Psychol. 43, 54–69 (2007). (PMID: 17201508359309310.1037/0012-1649.43.1.54)
Dijksterhuis, A. & Aarts, H. On wildebeests and humans: the preferential detection of negative stimuli. Psychol. Sci. 14, 14–18 (2003). (PMID: 1256474810.1111/1467-9280.t01-1-01412)
Müller-Pinzler, L. et al. Negativity-bias in forming beliefs about own abilities. Sci. Rep. 9, 14416 (2019). (PMID: 31594967678343610.1038/s41598-019-50821-w)
Boydstun, A. E., Ledgerwood, A. & Sparks, J. A negativity bias in reframing shapes political preferences even in partisan contexts. Soc. Psychol. Pers. Sci. 10, 53–61 (2019). (PMID: 10.1177/1948550617733520)
Ito, T. A., Larsen, J. T., Smith, N. K. & Cacioppo, J. T. Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. J. Pers. Soc. Psychol. 75, 887–900 (1998). (PMID: 982552610.1037/0022-3514.75.4.887)
Öhman, A. & Mineka, S. Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol. Rev. 108, 483–522 (2001). (PMID: 1148837610.1037/0033-295X.108.3.483)
Öhman, A., Flykt, A. & Esteves, F. Emotion drives attention: detecting the snake in the grass. J. Exp. Psychol. Gen. 130, 466–478 (2001). (PMID: 1156192110.1037/0096-3445.130.3.466)
Shoemaker, P. J. Hardwired for news: using biological and cultural evolution to explain the surveillance function. J. Commun. 46, 32–47 (1996). (PMID: 10.1111/j.1460-2466.1996.tb01487.x)
Stieglitz, S. & Dang-Xuan, L. Emotions and information diffusion in social media: sentiment of microblogs and sharing behavior. J. Manage. Inf. Syst. 29, 217–248 (2013). (PMID: 10.2753/MIS0742-1222290408)
Naveed, N., Gottron, T., Kunegis, J. & Alhadi, A. C. Proc. 3rd International Web Science Conference (Association for Computing Machinery, 2011).
Kim, J. & Yoo, J. 2012 International Conference on Social Informatics (IEEE, 2012).
Berger, J. & Milkman, K. L. What makes online content viral? J. Mark. Res. 49, 192–205 (2012). (PMID: 10.1509/jmr.10.0353)
Chuai, Y. & Zhao, J. Anger can make fake news viral online. Front. Phys. 10, 970174 (2022). (PMID: 10.3389/fphy.2022.970174)
Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A. & Van Bavel, J. J. Emotion shapes the diffusion of moralized content in social networks. Proc. Natl Acad. Sci. USA 114, 7313–7318 (2017). (PMID: 28652356551470410.1073/pnas.1618923114)
Pröllochs, N., Bär, D. & Feuerriegel, S. Emotions explain differences in the diffusion of true vs. false social media rumors. Sci. Rep. 11, 22721 (2021). (PMID: 34811397860892710.1038/s41598-021-01813-2)
Pröllochs, N., Bär, D. & Feuerriegel, S. Emotions in online rumor diffusion. EPJ Data Sci. 10, 51 (2021). (PMID: 10.1140/epjds/s13688-021-00307-5)
Zollo, F. et al. Emotional dynamics in the age of misinformation. PLoS ONE 10, e0138740 (2015). (PMID: 26422473458939510.1371/journal.pone.0138740)
Rathje, S., Van Bavel, J. J. & van der Linden, S. Out-group animosity drives engagement on social media. Proc. Natl Acad. Sci. USA 118, e2024292118 (2021). (PMID: 34162706825603710.1073/pnas.2024292118)
Soroka, S., Fournier, P. & Nir, L. Cross-national evidence of a negativity bias in psychophysiological reactions to news. Proc. Natl Acad. Sci. USA 116, 18888–18892 (2019). (PMID: 31481621675454310.1073/pnas.1908369116)
Trussler, M. & Soroka, S. Consumer demand for cynical and negative news frames. Int. J. Press Polit. 19, 360–379 (2014). (PMID: 10.1177/1940161214524832)
Meffert, M. F., Chung, S., Joiner, A. J., Waks, L. & Garst, J. The effects of negativity and motivated information processing during a political campaign. J. Commun. 25, 27–51 (2006). (PMID: 10.1111/j.1460-2466.2006.00003.x)
Bradley, S. D., Angelini, J. R. & Lee, S. Psychophysiological and memory effects of negative political ads: aversive, arousing, and well remembered. J. Advert. 36, 115–127 (2007). (PMID: 10.2753/JOA0091-3367360409)
Soroka, S. & McAdams, S. News, politics, and negativity. Polit. Commun. 32, 1–22 (2012). (PMID: 10.1080/10584609.2014.881942)
Lengauer, G., Esser, F. & Berganza, R. Negativity in political news: a review of concepts, operationalizations and key findings. Journalism 13, 179–202 (2012). (PMID: 10.1177/1464884911427800)
Jang, S. M. & Oh, Y. W. Getting attention online in election coverage: audience selectivity in the 2012 US presidential election. New Media Soc. 18, 2271–2286 (2016). (PMID: 10.1177/1461444815583491)
Haselmayer, M., Meyer, T. M. & Wagner, M. Fighting for attention: media coverage of negative campaign messages. Party Politics 25, 412–423 (2019). (PMID: 10.1177/1354068817724174)
Soroka, S. N. Good news and bad news: asymmetric responses to economic information. J. Polit. 68, 372–385 (2006). (PMID: 10.1111/j.1468-2508.2006.00413.x)
Matias, J., Munger, K., Le Quere, M. A. & Ebersole, C. The Upworthy Research Archive, a time series of experiments in U.S. media. Nat. Sci. Data 8, 195 (2021). (PMID: 10.1038/s41597-021-00934-7)
Karpf, D. Analytic Activism: Digital Listening and the New Political Strategy. Oxford Studies in Digital Politics (Oxford Univ. Press, 2016).
Thompson, D. I thought I knew how big Upworthy was on Facebook: then I saw this. The Atlantic https://www.theatlantic.com/business/archive/2013/12/i-thought-i-knew-how-big-upworthy-was-on-facebook-then-i-saw-this/282203/ (2012).
Fitts, A. S. The king of content: how Upworthy aims to alter the web, and could end up altering the world. Columbia J. Rev. https://archives.cjr.org/feature/the_king_of_content.php (2014).
Upworthy. How to make that one thing go viral. SlideShare https://www.slideshare.net/Upworthy/how-to-make-that-one-thing-go-viral-just-kidding/25 (2012).
Soroka, S., Young, L. & Balmas, M. Bad news or mad news? Sentiment scoring of negativity, fear, and anger in news content. Ann. Am. Acad. Political Soc. Sci. 659, 108–121 (2015). (PMID: 10.1177/0002716215569217)
Fox, E. et al. Facial expressions of emotion: are angry faces detected more efficiently? Cogn. Emot. 14, 61–92 (2000). (PMID: 17401453183977110.1080/026999300378996)
De Gelder, B. Towards the neurobiology of emotional body language. Nat. Rev. Neurosci. 7, 242–249 (2006). (PMID: 1649594510.1038/nrn1872)
Brady, W. J., Crockett, M. J. & Van Bavel, J. J. The MAD model of moral contagion: the role of motivation, attention, and design in the spread of moralized content online. Perspect. Psychol. Sci. 15, 978–1010 (2020). (PMID: 3251106010.1177/1745691620917336)
Spring, V. L., Cameron, C. D. & Cikara, M. The upside of outrage. Trends Cogn. Sci. 22, 1067–1069 (2018). (PMID: 3034098410.1016/j.tics.2018.09.006)
Mohammad, S. & Turney, P. Proc. NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (Association for Computational Linguistics, 2010).
Mohammad, S. M. in Emotion Measurement (ed. Meiselman, H. L.) 201–237 (Woodhead Publishing, 2016).
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D. & Kappas, A. Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61, 2544–2558 (2010). (PMID: 10.1002/asi.21416)
Toetzke, M., Banholzer, N. & Feuerriegel, S. Monitoring global development aid with machine learning. Nat. Sustain. 5, 533–541 (2022). (PMID: 10.1038/s41893-022-00874-z)
Lakens, D., Scheel, A. M. & Isager, P. M. Equivalence testing for psychological research: a tutorial. Adv. Methods Pract. Psychol. Sci. 1, 259–269 (2018). (PMID: 10.1177/2515245918770963)
Plutchik, R. Emotion: Theory, Research, and Experience 2nd edn (Academic Press, 1984).
Ekman, P. An argument for basic emotions. Cogn. Emot. 6, 169–200 (1992). (PMID: 10.1080/02699939208411068)
Van Bavel, J. J., Rathje, S., Harris, E., Robertson, C. & Sternisko, A. How social media shapes polarization. Trends Cogn. Sci. 25, 913–916 (2021). (PMID: 3442925510.1016/j.tics.2021.07.013)
Barrett, L. F. & Russell, J. A. (eds) The Psychological Construction of Emotion (Guilford Publications, 2014).
Bachleda, S. et al. Individual-level differences in negativity biases in news selection. Pers. Individ. Dif. 155, 109675 (2020). (PMID: 10.1016/j.paid.2019.109675)
Kross, E. et al. Does counting emotion words on online social networks provide a window into people’s subjective experience of emotion? A case study on Facebook. Emotion 19, 97–107 (2019). (PMID: 2962038410.1037/emo0000416)
Jakubik, J., Vössing, M., Bär, D., Pröllochs, N. & Feuerriegel, S. Online emotions during the storming of the US Capitol: evidence from the social media network Parler. In Proc. International Conference on Web and Social Media (ICWSM) (2023).
Barrett, L. F. Discrete emotions or dimensions? The role of valence focus and arousal focus. Cogn. Emot. 12, 579–599 (1998). (PMID: 10.1080/026999398379574)
Cohen, J. Statistical Power Analysis for the Behavioral Sciences 2nd edn (Lawrence Erlbaum Associates Publishers, 2013).
Pennebaker, J. W., Boyd, R. L., Jordan, K. & Blackburn, K. The development and psychometric properties of LIWC2015. Univ. Texas Libraries http://hdl.handle.net/2152/31333 (2015).
Song, H. et al. In validations we trust? The impact of imperfect human annotations as a gold standard on the quality of validation of automated content analysis. Polit. Commun. 37, 550–572 (2020). (PMID: 10.1080/10584609.2020.1723752)
Gunning, R. The Technique of Clear Writing (McGraw-Hill, 1952).
Richardson, M., Dominowska, E. & Ragno, R. Predicting clicks: estimating the click-through rate for new ads. In Proc 16th International Conference on World Wide Web 521–530 (2007).
Sauter, D. A., Eisner, F., Ekman, P. & Scott, S. K. Cross-cultural recognition of basic emotions through nonverbal emotional vocalizations. Proc. Natl Acad. Sci. USA 107, 2408–2412 (2010). (PMID: 20133790282386810.1073/pnas.0908239106)
Khalilzadeh, J. & Tasci, A. D. Large sample size, significance level, and the effect size: solutions to perils of using big data for academic research. Tour. Manag. 62, 89–96 (2017). (PMID: 10.1016/j.tourman.2017.03.026)

Date Created: 20230317 Date Completed: 20230526 Latest Revision: 20240703

20240704

PMC10202797

10.1038/s41562-023-01538-4

36928780