References
- Abramowitz, A. I., & Webster, S. (2016). The rise of negative partisanship and the nationalization of U.S. elections in the 21st century. Electoral Studies, 41, 12–22. https://doi.org/https://doi.org/10.1016/j.electstud.2015.11.001
- Ansolabehere, S., & Iyengar, S. (1997). Going negative: How political advertisements shrink and polarize the electorate. Free Press.
- Barberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using twitter data. Political Analysis, 23(1), 76–91. https://doi.org/https://doi.org/10.1093/pan/mpu011
- Barberá, P., Casas, A., Nagler, J., Egan, P. J., Bonneau, R., Jost, J. T., & Tucker, J. A. (2019). Who leads? Who follows? Measuring issue attention and agenda setting by legislators and the mass public using social media data. American Political Science Review, 113(4), 883–901. https://doi.org/https://doi.org/10.1017/S0003055419000352
- 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/https://doi.org/10.1177/0956797615594620
- Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370. https://doi.org/https://doi.org/10.1037/1089-2680.5.4.323
- Benoit, K., Nulty, P., Müller, S., Obeng, A., Watanabe, K., & Matsuo, A. (2018). quanteda: An R package for the quantitative analysis of. Journal of Open Source Software, 30(3), 774–778. https://doi.org/https://doi.org/10.21105/joss.00774
- Boydstun, A. E., Ledgerwood, A., & Sparks, J. (2019). A negativity bias in reframing shapes political preferences even in partisan contexts. Social Psychological and Personality Science, 10(1), 53–61. https://doi.org/https://doi.org/10.1177/1948550617733520
- Brooks, D. J. (2006). The resilient voter: Moving toward closure in the debate over negative campaigning and turnout. Journal of Politics, 68(3), 684–696. https://doi.org/https://doi.org/10.1111/j.1468-2508.2006.00454.x
- Carroll, R., & Kubo, H. (2019). Measuring and comparing party ideology and heterogeneity. Party Politics, 25(2), 245–256. https://doi.org/https://doi.org/10.1177/1354068817710222
- Damore, D. F. (2002). Candidate strategy and the decision to go negative. Political Research Quarterly, 55(3), 669–685. https://doi.org/https://doi.org/10.1177/106591290205500309
- de Neve, J. E. (2011). The median voter data set: Voter preferences across 50 democracies. Electoral Studies, 30(4), 865–871. https://doi.org/https://doi.org/10.1016/j.electstud.2011.09.005
- Diermeier, D., & Li, C. (2019). Partisan affect and elite polarization. American Political Science Review, 113(1), 277–281. https://doi.org/https://doi.org/10.1017/S0003055418000655
- Djupe, P. A., & Peterson, D. A. M. (2002). The impact of negative campaigning: Evidence from the 1998 senatorial primaries. Political Research Quarterly, 55(4), 845–860. https://doi.org/https://doi.org/10.1177/106591290205500406
- Enli, G. S., & Skogerbø, E. (2013). Personalized campaigns in party-centred politics. Information, Communication & Society, 16(5), 757–774. https://doi.org/https://doi.org/10.1080/1369118X.2013.782330
- FiveThirtyEight Methodology (2018, October 18). How FiveThirtyEight's house, senate and governor models work. Retrieved October 19, 2018, from https://fivethirtyeight.com/methodology/how-fivethirtyeights-house-and-senate-models-work/
- Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models (1st publ ed.). Cambridge University Press. http://www.loc.gov/catdir/enhancements/fy0668/2006040566-d.html
- Golder, M., Gschwend, T., Crabtree, C., & Indirdason, I. (2017). Campaign sentiment in European party manifestos.
- Goldstein, K., & Freedman, P. (2002). Campaign advertising and voter turnout: New evidence for a stimulation effect. Journal of Politics, 64(3), 721–740. https://doi.org/https://doi.org/10.1111/0022-3816.00143
- Grant, W. J., Moon, B., & Busby Grant, J. (2010). Digital dialogue? Australian politicians' use of the social network tool twitter. Australian Journal of Political Science, 45(4), 579–604. https://doi.org/https://doi.org/10.1080/10361146.2010.517176
- Gross, J., & Johnson, K. (2016). Twitter taunts and tirades: Negative campaigning in the age of trump. Political Science & Politics, 49(4), 748–754. https://doi.org/https://doi.org/10.1017/S1049096516001700
- Habeck, R. (2019, January 7). Bye bye, twitter und Facebook: Ein Blog zum Abschied. Retrieved February 9, 2919, from https://www.robert-habeck.de/texte/blog/bye-bye-twitter-und-facebook/
- Hopp, T., & Vargo, C. (2017). Does negative campaign advertising stimulate uncivil communication on social media? Measuring audience response using big data. Computers in Human Behavior, 68, 368–377. https://doi.org/https://doi.org/10.1016/j.chb.2016.11.034
- Iyengar, S., Ansolabehere, S., Simon, A., & Valentino, N. (1994). Does attack advertising demobilize the electorate? American Political Science Review, 88(4), 829–838. https://doi.org/https://doi.org/10.2307/2082710
- Iyengar, S., & Hahn, K. S. (2009). Red media, blue media: Evidence of ideological selectivity in media use. Journal of Communication, 59(1), 19–39. https://doi.org/https://doi.org/10.1111/jcom.2009.59.issue-1
- Jungherr, A. (2015). Analyzing political communication with digital trace data: The role of Twitter messages in social science research. Springer. https://doi.org/10.1007/978-3-319-20319-5 .
- Jungherr, A. (2016). Twitter use in election campaigns: A systematic literature review. Journal of Information Technology & Politics, 13(1), 72–91. https://doi.org/https://doi.org/10.1080/19331681.2015.1132401
- Jungherr, A., Schoen, H., Posegga, O., & Jürgens, P. (2016). Digital trace data in the study of public opinion. Social Science Computer Review, 35(3), 336–356. https://doi.org/https://doi.org/10.1177/0894439316631043
- Kahn, K. F., & Kenney, P. J. (1999). Do negative campaigns mobilize or suppress turnout? Clarifying the relationship between negativity and participation. American Political Science Review, 93(4), 877–889. https://doi.org/https://doi.org/10.2307/2586118
- Kearney, M. (2018). rtweet: An implementation of calls designed to collect and organize Twitter data via Twitter's REST and stream application program interfaces (0.6.6).
- Kreiss, D., Lawrence, R. G., & McGregor, S. C. (2018). In their own words: Political practitioner accounts of candidates, audiences, affordances, genres, and timing in strategic social media use. Political Communication, 35(1), 8–31. https://doi.org/https://doi.org/10.1080/10584609.2017.1334727
- Lau, R., & Rovner, I. B. (2009). Negative campaigning. Annual Review of Political Science, 12(1), 285–306. https://doi.org/https://doi.org/10.1146/annurev.polisci.10.071905.101448
- Lau, R., Sigelman, L., & Rovner, I. B. (2007). The effects of negative political campaigns: A meta-analytic reassessment. Journal of Politics, 69(4), 1176–1209. https://doi.org/https://doi.org/10.1111/j.1468-2508.2007.00618.x
- Martin, P. (2004). Inside the black box of negative campaign effects: Three reasons why negative campaigns mobilize. Political Psychology, 25(4), 545–562. https://doi.org/https://doi.org/10.1111/pops.2004.25.issue-4
- Mason, L. (2015). ‘I disrespectfully agree’: The differential effects of partisan sorting on social and issue polarization. American Journal of Political Science, 59(1), 128–145. https://doi.org/https://doi.org/10.1111/ajps.2015.59.issue-1
- Mazzeloni, G., & Schulz, W. (1999). ‘Mediatization’ of politics: A challenge for democracy? Political Communication, 16(3), 247–261. https://doi.org/https://doi.org/10.1080/105846099198613
- Meyer, T. M., Haselmayer, M., & Wagner, M. (2017). Who gets into the papers? Party campaign messages and the media. British Journal of Political Science, 64, 1–22.
- Mohammad, S. (2016). A practical guide to sentiment annotation: Challenges and solutions. In A. Balahur, E. van der Goot, P. Vossen, & A. Montoyo (Eds.), Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 174–179). Association for Computational Linguistics. https://doi.org/https://doi.org/10.18653/v1/W16-0429
- Nord, L., & Grussell, M. (2012). Three attitudes to 140 characters: The use and views of twitter in political party communications in Sweden. Public Communication Review,, 2(2), 48–62.
- Ott, B. (2016). The age of twitter: Donald J. Trump and the politics of debasement. Critical Studies in Media Communication, 34(1), 59–68. https://doi.org/https://doi.org/10.1080/15295036.2016.1266686
- Ott, L., & Theunissen, P. (2015). Reputations at risk: Engagement during social media crises. Public Relations Review, 41(1), 97–102. https://doi.org/https://doi.org/10.1016/j.pubrev.2014.10.015
- Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin Press.
- Peterson, D., & Djupe, P. (2005). When primary campaigns go negative: The determinants of campaign negativity. Political Research Quarterly, 58(1), 45–54. https://doi.org/https://doi.org/10.1177/106591290505800104
- Ridout, T., & Walter, A. (2015). How the news media amplify negative messages. In A. Nai & A. Walter (Eds.), New perspectives on negative campaigning (pp. 267–286). ECPR Press.
- Rogowski, J., & Sutherland, J. (2016). How ideology fuels affective polarization. Political Behavior, 38(2), 485–508. https://doi.org/https://doi.org/10.1007/s11109-015-9323-7
- Ross, A., & Caldwell, D. (2020). ‘Going negative’: An APPRAISAL analysis of the rhetoric of Donald Trump on twitter. Language & Communication, 70, 13–27. https://doi.org/https://doi.org/10.1016/j.langcom.2019.09.003
- Soroka, S. (2012). The gatekeeping function: Distributions of information in media and the real world. The Journal of Politics, 74(2), 514–528. https://doi.org/https://doi.org/10.1017/S002238161100171X
- Soroka, S., & McAdams, S. (2015). News, politics, and negativity. Political Communication, 32(1), 1–22. https://doi.org/https://doi.org/10.1080/10584609.2014.881942
- 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/https://doi.org/10.2753/MIS0742-1222290408
- Stier, S., Bleier, A., Lietz, H., & Strohmaier, M. (2018). Election campaigning on social media: Politicians, audiences, and the mediation of political communication on facebook and twitter. Political Communication, 35(1), 50–74. https://doi.org/https://doi.org/10.1080/10584609.2017.1334728
- Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010). Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In 2010 IEEE second international conference on social computing (pp. 177–184). IEEE. https://doi.org/https://doi.org/10.1109/SocialCom.2010.33
- Young, L., & Soroka, S. (2012). Affective news: The automated coding of sentiment in political texts. Political Communication, 29(2), 205–231. https://doi.org/https://doi.org/10.1080/10584609.2012.671234