Publications

2020

Araujo, T. B., & van der Meer, T. G. L. A. (2020). News values on social media: Exploring what drives peaks in user activity about organizations on Twitter. Journalism21, 633-651. doi: 10.1177/1464884918809299

Araujo, T. B. (2020). Conversational agent research toolkit: An alternative for creating and managing chatbots for experimental research. Computational Communication Research2, 35-51. doi: 10.5117/CCR2020.1.002.ARAU

Araujo, T. B., Lock, I. J., & van de Velde, R. N. (2020). Automated Visual Content Analysis (AVCA) in Communication Research: A Protocol for Large Scale Image Classification with Pre-Trained Computer Vision Models. Communication Methods and Measures. doi: 10.1080/19312458.2020.1810648

Boukes, M., van de Velde, B., Araujo, T., & Vliegenthart, R. (2020). What’s the Tone? Easy Doesn’t Do It: Analyzing Performance and Agreement Between Off-the-Shelf Sentiment Analysis Tools. Communication Methods and Measures14(2), 83-104. doi: 10.1080/19312458.2019.1671966

Ohme, J., Araujo, T. B., de Vreese, C. H., & Piotrowski, J. (2020). Mobile data donations: Assessing self-report accuracy and sample biases with the iOS Screen Time function. Mobile Media & Communication. doi: 10.1177/2050157920959106

Vermeer, S. A. M., Kruikemeier, S., Trilling, D. C., & de Vreese, C. H. (2020). WhatsApp with Politics?!: Examining the Effects of Interpersonal Political Discussion in Instant Messaging Apps. The International Journal of Press/Politics. doi: 10.1177/1940161220925020

Gerken, F., & van der Meer, T. G. L. A. (Accepted/In press). Crises frame dynamics: Frame diversity in news media and the role of governmental actors. Journal of International Crisis and Risk Communication Research2(2), 149-180. doi: 10.30658/jicrcr.2.2.1

Trilling, D. & Van Hoof, M. (2020). Between Article and Topic: News Events as Level of Analysis and Their Computational Identification. Digital Journalism (online first). doi: 10.1080/21670811.2020.1839352

Williams, N. W., Casas, A., & Wilkerson, J. D. (2020). Images as Data for Social Science Research: An Introduction to Convolutional Neural Nets for Image Classification. Cambridge University Press.

Casas, A., Denny, M. J., & Wilkerson, J. (2020). More effective than we thought: Accounting for legislative hitchhikers reveals a more inclusive and productive lawmaking process. American Journal of Political Science64(1), 5-18. doi: 10.1111/ajps.12472

Kroon, A. C., Trilling, D., & Raats, T. (2020). Guilty by Association: Using Word Embeddings to Measure Ethnic Stereotypes in News Coverage. Journalism and Mass Communication Quarterly (online first). doi: 10.1177/1077699020932304

Vermeer, S. A. M., Trilling, D., Kruikemeier, S., & de Vreese, C. H. (2020). Online news user journeys: The role of social media, news websites, and topics. Digital Journalism (online first). doi: 10.1080/21670811.2020.1767509

Burggraaff, C., & Trilling, D. (2020). Through a different gate: An automated content analysis of how online news and print news differ. Journalism21, 112-129. doi: 10.1177/1464884917716699

Denkovski, O., & Trilling, D. (2020). Whose fingerprint does the news show? Developing machine learning classifiers for automatically identifying Russian state-funded news in Serbia. International Journal of Communication : IJoC14, 4428-4452.

Loecherbach, F., & Trilling, D. (2020). 3bij3: Developing a framework for researching recommender systems and their effects. Computational Communication Research2, 53- 79. doi: 10.5117/CCR2020.1.003.LOEC

Vermeer, S. A. M., & Trilling, D. (2020). Toward a better understanding of news user journeys: A markov chain approach. Journalism Studies21, 879-894. doi: 10.1080/1461670X.2020.1722958


2019

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 Review113(4), 883-901. doi: 10.1017/S0003055419000352

Vermeer, S. A. M., & Araujo, T. B. (2019). Keep the ball rolling: Information diffusion within large sports-related networks through social mediators. Communication & Sport8(6), 803-824. doi: 10.1177/2167479519841868

Vermeer, S. A. M., Araujo, T., Bernritter, S. F., & van Noort, G. (2019). Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media. International Journal of Research in Marketing36(3), 492-508. doi: 10.1016/j.ijresmar.2019.01.010

van der Meer, T. G. L. A., Kroon, A. C., Verhoeven, P., & Jonkman, J. (2019). Mediatization and the disproportionate attention to negative news: The case of airplane crashes. Journalism Studies20(6), 783-803. doi: 10.1080/1461670X.2018.1423632 

van Atteveldt, W., Strycharz, J., Trilling, D., & Welbers, K. (2019). Toward open computational communication science: A practical road map for reusable data and code. International Journal of Communication : IJoC13, 3935-3954. [details]

Kroon, A. C., Trilling, D. C., van der Meer, G. L. A., & Jonkman, J. G. F. (2019). Clouded reality: News representations of culturally close and distant ethnic outgroups. Communications : The European Journal of Communication Research. doi: 10.1515/commun-2019-2069

Kroon, A. C., Trilling, D., Van Selm, M., & Vliegenthart, R. (2019). Biased media? How news content influences age discrimination claims. European journal of Ageing16(1), 109–119. doi: 10.1007/s10433-018-0465-4

Vasko, V., & Trilling, D. (2019). A permanent campaign? Tweeting differences among members of Congress between campaign and routine periods. Journal of Information Technology and Politics16(4), 342-359. doi: 10.1080/19331681.2019.1657046


2018

Trilling, D. & Jonkman, J.G.F. (2018). Scaling up Content Analysis. Communication Methods and Measures (online first). doi: 10.1080/19312458.2018.1447655

van der Meer, T. G. L. A. (2018). Public frame building: The role of source usage in times of crisis. Communication Research45(6), 956-981. doi: 10.1177/0093650216644027

Kroon, A. C., & van der Meer, T. G. L. A. (2018). Who takes the lead? Investigating the reciprocal relationship between organizational and news agendas. Communication Research. doi: 10.1177/0093650217751733

Trilling, D. (2018). Big Data, Analysis of. In: Matthes, J. (ed.), International Encyclopedia of Communication Research Methods. Hoboken, NJ: Wiley.

Günther, E., Trilling, D., & Van de Velde, R.N. (2018). But how do we store it? (Big) data architecture in the social-scientific research process. In: Stuetzer, C.M., Welker, M., & Egger, M. (eds.): Computational Social Science in the Age of Big Data. Concepts, Methodologies, Tools, and Applications. Cologne, Germany: Herbert von Halem. (pp. 161-187)

Möller, J., Trilling, D., Helberger, N., & van Es, B. (2018). Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity. Information, Communication & Society (online first). doi: 10.1080/1369118x.2018.1444076

Strycharz, J., Strauss, N., & Trilling, D. (2018). The role of media coverage in explaining stock market fluctuations: Insights for strategic financial communication. International Journal of Strategic Communication12(1), 67–85. doi: 10.1080/1553118X.2017.1378220


2017

Wilkerson, J., & Casas, A. (2017). Large-scale computerized text analysis in political science: Opportunities and challenges. Annual Review of Political Science20, 529-544. doi: 10.1146/annurev-polisci-052615-025542

Welbers, K., van Atteveldt, W. & Benoit, K. (2017). Text Analysis in R. Communication Methods and Measures, 11 (4), 245-265, doi: 10.1080/19312458.2017.1387238

Araujo, T., Wonneberger, A., Neijens, P., & de Vreese, C. (2017). How much time do you spend online? Understanding and improving the accuracy of self-reported measures of internet use. Communication Methods and Measures11(3), 173-190. doi: 10.1080/19312458.2017.1317337

Van Atteveldt, W., Sheafer, T., Shenhav, S. R. & Fogel-Dror, Y. (2017). Clause analysis: Using syntactic information to automatically extract source, subject, and predicate from texts with an application to the 2008-2009 Gaza War. Political Analysis, 25(2), 207-222. doi: 10.1017/pan.2016.12

Boukes, M., & Trilling, D. (2017) Political relevance in the eye of the beholder: Determining the substantiveness of TV shows and political debates with Twitter data. First Monday, 22(4). doi: 10.5210/fm.v22i14.7031

Burggraaff, C. & Trilling (2017). Through a different gate: An automated content analysis of how online news and print news differ. Journalism, online first. doi: 10.1177/1464884917716699 [HTML] [PDF]

Trilling, D., Tolochko, P., & Burscher, B. (2017). From newsworthiness to shareworthiness: How to predict news sharing based on article characteristics. Journalism & Mass Communication Quarterly94(1), 38-60. doi: 10.1177/1077699016654682

Vermeer, S., Remmelswaal, P., & Jacobs, S. (2017). Heineken in the House: Improving Online Media Reputation through Featuring a Sponsored Brand Community. Communication Management Review2(1), 76-103. doi: 10.22522/cmr20170117


2016

Boumans, J.W., & Trilling, D. (2016). Taking stock of the toolkit: An overview of relevant automated content analysis approaches and techniques for digital journalism scholars. Digital Journalism, 4(1), 8–23. doi: 10.1080/21670811.2015.1096598

Gerken, F., Van der Land, S. F., & van der Meer, T. G. L. A. (2016). Crisis in the Air: An investigation of AirAsia’s crisis-response effectiveness based on frame alignment. Public Relations Review42(5), 879-892. doi: 10.1016/j.pubrev.2016.09.002

van Zoonen, W., & van der Meer, T. G. L. A. (2016).Social media research: The application of supervised machine learning in organizational communication research. Computers in Human Behavior63, 132-141. doi: 10.1016/j.chb.2016.05.028

van der Meer, T. G. L. A. (2016). Automated content analysis and crisis communication research. Public Relations Review42(5), 952-961. doi: 10.1016/j.pubrev.2016.09.001


2015

Trilling, D. (2015) Two different debates? Investigating the relationship between a political debate on TV and simultaneous comments on Twitter. Social Science Computer Review, 33(3), 259–276. doi: 10.1177/0894439314537886.