Present, endorsed, and active: Instagram cues that predict trust

References

• Bellur, S., & Sundar, S. S. (2014). How can we tell when a heuristic has been used? Design and analysis strategies for capturing the operation of heuristics. Communication Methods and Measures, 8, 116–137. doi:10.1080/19312458.2014.903390
   Google Scholar
• Bistaffa, F., Farinelli, A., Chalkiadakis, G., & Ramchurn, S. (2017). A cooperative game- theoretic approach to the social ridesharing problem. Artificial Intelligence, 246, 86–117. doi:10.1016/j.artint.2017.02.004
   Web of Science ®Google Scholar
• Botsman, R. (2017). Who can you trust?: How technology brought us together and why it could drive us apart. London, UK: Penguin.
   Google Scholar
• Hosseinmardi, H., Mattson, S., Rafiq, R., Han, R., Lv, Q., & Mishra, S. (2015). Analyzing labeled cyberbullying incidents on the Instagram social network. Proceedings of the 7th International Conference on Social Informatics, LNCS 9471, Beijing, China, 49–66. doi:10.1007/978-3-319-27433-1_4
   Google Scholar
• Kim, S., Lee, J., & Yoon, D. (2015). Norms in social media: The application of theory of reasoned action and personal norms in predicting interactions with Facebook page like ads. Communication Research Reports, 32, 322–331. doi:10.1080/08824096.2015.1089851
   Web of Science ®Google Scholar
• 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, 8–31. doi:10.1080/10584609.2017.1334727
   Web of Science ®Google Scholar
• Li, G., Wang, J., Zheng, Y., & Franklin, M. (2016). Crowdsourced data management: A survey. IEEE Transactions on Knowledge and Data Engineering, 28, 2296–2319. doi:10.1109/TKDE.2016.2535242
   Web of Science ®Google Scholar
• Ma, R., & Zhang, H. (2017). The morning commute problem with ridesharing and dynamic parking charges. Transportation Research Part B: Methodological, 106, 345–374. doi:10.1016/j.trb.2017.07.002
   Web of Science ®Google Scholar
• McGlynn, J., & McGlone, M. S. (2019). Desire or disease? Framing obesity to influence attributions of responsibility and policy support. Health Communication, 34, 689-701. doi:10.1080/10410236.2018.1431025.
   PubMed Web of Science ®Google Scholar
• Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59, 210–220. doi:10.1016/j.pragma.2013.07.012
   Web of Science ®Google Scholar
• Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and heuristic approaches to credibility online. Journal of Communication, 60, 413–439. doi:10.1111/j.1460-2466.2010.01488.x
   Web of Science ®Google Scholar
• Mittendorf, C. (2017). The implications of trust in the sharing economy: An empirical analysis of Uber. Proceedings of the 50th Hawaii International Conference on System Sciences, 5837–5846. http://hdl.handle.net/10125/41866
   Google Scholar
• Mousavi, S., & Gigerenzer, G. (2014). Risk, uncertainty, and heuristics. Journal of Business Research, 67, 1671–1678. doi:10.1016/j.jbusres.2014.02.013
   Web of Science ®Google Scholar
• Niederdeppe, J. (2016). Meeting the challenge of measuring communication exposure in the digital age. Communication Methods and Measures, 10, 170–172. doi:10.1080/19312458.2016.1150970
   Web of Science ®Google Scholar
• Park, H. S., Dailey, R., & Lemus, D. (2002). The use of exploratory factor analysis and principal components analysis in communication research. Human Communication Research, 28, 562–577. doi:10.1111/hcre.2002.28.issue-4
   Web of Science ®Google Scholar
• Peña, J., Khan, S., Burrows, C. N., & Blanton, H. (2018). How persuasive are health advertisements in first-person shooter games? Exploring knowledge-activation and thought-disruption mechanisms. Communication Research Reports, 35, 293–302. doi:10.1080/08824096.2018.1469484
   Web of Science ®Google Scholar
• Slovic, P., & Peters, E. (2006). Risk perception and affect. Current Directions in Psychological Science, 15, 322–325. doi:10.1111/j.1467-8721.2006.00461.x
   Web of Science ®Google Scholar
• Stiglic, M., Agatz, N., Savelsbergh, M., & Gradisar, M. (2016). Making dynamic ride-sharing work: The impact of driver and rider flexibility. Transportation Research Part E: Logistics and Transportation Review, 91, 190–207. doi:10.1016/j.tre.2016.04.010
   Web of Science ®Google Scholar
• Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In M. Metzger & A. J. Flanagin (Eds.), Digital media, youth, and credibility (pp. 73–100). Cambridge, MA: The MIT Press.
   Google Scholar
• Sundar, S. S., Jia, H., Waddell, T. F., & Huang, Y. (2015). Toward a theory of interactive media effects (TIME). In S. Sundar (Ed.), The handbook of the psychology of communication technology (pp. 47–86). Oxford, UK: John Wiley & Sons.
   Google Scholar
• Vendemia, M. A. (2017). (Re)viewing reviews: Effects of emotionality and valence on credibility perceptions in online consumer reviews. Communication Research Reports, 34, 230–238. doi:10.1080/08824096.2017.1286470
   Web of Science ®Google Scholar
• Westerman, D., Spence, P. R., & Van Der Heide, B. (2014). Social media as information source: Recency of updates and credibility of information. Journal of Computer-mediated Communication, 19, 171–183. doi:10.1111/jcc4.12041
   Web of Science ®Google Scholar
• Yan, Y., Rosales, R., Fung, G., & Dy, J. (2011). Active learning from crowds. Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA. 1161-1168.
   Google Scholar
• Yilmaz, G., & Quintero Johnson, J. M. (2016). Tweeting facts, Facebooking lives: The influence of language use and modality on online source credibility. Communication Research Reports, 33, 137–144. doi:10.1080/08824096.2016.1155047
   Web of Science ®Google Scholar