March 1, 2020
A Hubner, J McKnight, M Sweitzer, & R Bond
Computational Communication Research
Digital trace data enable researchers to study communication processes at a scale previously impossible. We combine social network analysis and automated content analysis to examine source and message factors’ impact on ratings of user-shared content. We found that the expertise of the author, the network position that the author occupies, and characteristics of the content the author creates have a significant impact on how others respond to that content. By observationally examining a large-scale online community, we provide a real-world test of how message consumers react to source and message characteristics. Our results show that it is important to think of online communication as occurring interactively between networks of individuals, and that the network positions people inhabit may inform their behavior.
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