The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Conceptualizing sources in online news
Abstract
This study attempts a new conceptualization of communication “sources” by proposing a typology of sources that would apply not only to traditional media but also to new online media. Ontological rationale for the distinctions in the typology is supplemented by psychological evidence via an experiment that investigated the effects of different types of source attributions upon receivers' perception of online news content. Participants (N=48) in a 4-condition, between-participants experiment read 6 identical news stories each through an online service. Participants were told that the stories were selected by 1 of 4 sources: news editors, the computer terminal on which they were accessing the stories, other audience members (or users) of the online news service, or (using a pseudo-selection task) the individual user (self). After reading each online news story, all participants filled out a paper-and-pencil questionnaire indicating their perceptions of the story they had just read. In confirmation of the distinctions made in the typology, attribution of identical content to 4 different types of online sources was associated with significant variation in news story perception. Theoretical implications of the results as well as the typology are discussed.
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Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 2×
“These researchers argue that technologies like computers invite ethopœia because of advanced features such as the use of language and voice, interactivity, and filling of roles traditionally held by humans ( Nass & Moon, 2000; Nass, Steuer, Henriksen, & Dryer, 1994).”
BackgroundSelf-citationThe Influence of Source Credibility on Communication Effectiveness
Hovland, C. I., & Weiss, W. (1951) · Public Opinion Quarterly · doi:10.1086/266350 · Cited by 3,543
Mixing Humans and Nonhumans Together: The Sociology of a Door-Closer
Johnson, J. (1988) · Social Problems · doi:10.2307/800624 · Cited by 892
Gratifications Sought and Media Exposure: An Expectancy Value Model
PALMGREEN, P., & RAYBURN, J. D. (1982) · Communication Research · doi:10.1177/009365082009004004 · Cited by 243 · cited 1×
“The idea of expectations leading to communication consequences is echoed in the social learning literature (e.g., Bandura, 1965, 1971) as well as uses and gratifications research (e.g., Palmgreen & Rayburn, 1982; Babrow, 1989; Babrow & Swanson, 1988).”
Background
Cited by
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338
Uses and Grats 2.0: New Gratifications for New Media
Sundar, S. S., & Limperos, A. M. (2013) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2013.845827 · Cited by 1,070
Self-citationIn AI we trust? Perceptions about automated decision-making by artificial intelligence
Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020) · AI & SOCIETY · doi:10.1007/s00146-019-00931-w · Cited by 1,038
Messing, S., & Westwood, S. J. (2014) · Communication Research · doi:10.1177/0093650212466406 · Cited by 902
Social Media as Information Source: Recency of Updates and Credibility of Information
Westerman, D., Spence, P. R., & Van Der Heide, B. (2014) · Journal of Computer-Mediated Communication · doi:10.1111/jcc4.12041 · Cited by 708
Personalization versus Customization: The Importance of Agency, Privacy, and Power Usage
Sundar, S. S., & Marathe, S. S. (2010) · Human Communication Research · doi:10.1111/j.1468-2958.2010.01377.x · Cited by 588
Self-citation“Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content
Molina, M. D., Sundar, S. S., Le, T., & Lee, D. (2021) · American Behavioral Scientist · doi:10.1177/0002764219878224 · Cited by 485
Self-citationKalyanaraman, S., & Sundar, S. S. (2006) · Journal of Communication · doi:10.1111/j.1460-2466.2006.00006.x · Cited by 475
Self-citationKöbis, N., & Mossink, L. D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106553 · Cited by 406
Enter the Robot Journalist: Users' perceptions of automated content
Clerwall, C. (2014) · Journalism Practice · doi:10.1080/17512786.2014.883116 · Cited by 377
Credibility: A multidisciplinary framework
Rieh, S. Y., & Danielson, D. R. (2007) · Annual Review of Information Science and Technology · doi:10.1002/aris.2007.1440410114 · Cited by 336
The New Personal Influence: How Our Facebook Friends Influence the News We Read
Anspach, N. M. (2017) · Political Communication · doi:10.1080/10584609.2017.1316329 · Cited by 289
Factors Influencing Information credibility on Social Media Platforms: Evidence from Facebook Pages
Li, R., & Suh, A. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.146 · Cited by 225
“Medium credibility refers to the perceived levels of credibility a specific medium that individuals used [12, 13].”
BackgroundBorah, P. (2014) · Communication Research · doi:10.1177/0093650212449353 · Cited by 206
Voices in and of the machine: Source orientation toward mobile virtual assistants
Guzman, A. L. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.08.009 · Cited by 199
Armstrong, C. L., & McAdams, M. J. (2009) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2009.01448.x · Cited by 179
Edwards, A., Edwards, C., Spence, P. R., Harris, C., & Gambino, A. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.06.005 · Cited by 174
Keding, C., & Meissner, P. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120970 · Cited by 168
Source Cues in Online News: Is the Proximate Source More Powerful than Distal Sources?
Kang, H., Bae, K., Zhang, S., & Sundar, S. S. (2011) · Journalism & Mass Communication Quarterly · doi:10.1177/107769901108800403 · Cited by 122
Self-citationMediated misinformation: Questions answered, more questions to ask
Lee, E.-J., & Shin, S. Y. (2021) · American Behavioral Scientist · doi:10.1177/0002764219869403 · Cited by 50
The Effects of Perceived AI Use On Content Perceptions
Rae, I. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642076 · Cited by 42
Value attributed to text-based archives generated by artificial intelligence
Darda, K., Carre, M., & Cross, E. (2023) · Royal Society Open Science · doi:10.1098/rsos.220915 · Cited by 37
“These challenges further necessitate research on how audiences perceive and evaluate algorithmically generated news articles compare with those written by human journalists [6,20,21].”
BackgroundMental health service use among young adults: A communication framework for program development
Munson, M. R., & Jaccard, J. (2018) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-016-0765-y · Cited by 28
Relevance versus big numbers: Students’ criteria for selecting scholarly references online.
Rouet, J.-F., Skov, O., De Pereyra, G., Ros, C., Le Bigot, L., & Vibert, N. (2018) · Journal of Experimental Psychology: Applied · doi:10.1037/xap0000194 · Cited by 6
Metadata
- Authors
- Sundar, S.
- Venue
- Journal of Communication
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-03-01
- Volume
- 51
- Issue
- 1
- Pages
- 52-72
- ISSN
- 0021-9916 (ISSN-L)
- 1460-2466
- OpenAlex
- W2042922813
- Cited by
- 530
- References
- 5
- FWCI
- 21.13
- Type
- journal article
- Language
- en
- Open access
- closed
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