[ATTW-L] New CDQ article on the Use of Machine Learning for Sorting Online Comments

[Luke Thominet]

Dear Colleagues,

CDQ has a new Online First article by Junzhe Zhu, Elizabeth Wickes, and John R. Gallagher, "A Machine Learning Algorithm for Sorting Online Comments via Topic Modeling<https://sigdoc.acm.org/cdq/a-machine-learning-algorithm-for-sorting-online-comments-via-topic-modeling/>" (sigdoc.acm.org/cdq/a-machine-learning-algorithm-for-sorting-online-comments-via-topic-modeling/).

Article Abstract: This article uses a machine learning algorithm to demonstrate a proof-of-concept case for moderating and managing online comments as a form of content moderation, which is an emerging area of interest for technical and professional communication (TPC) researchers. The algorithm sorts comments by topical similarity to a reference comment/article rather than display comments by linear time and popularity. This approach has the practical benefit of enabling TPC researchers to reconceptualize content display systems in dynamic ways.

We invite you to contribute to the conversation. Communication Design Quarterly strives to be a place open to all types of research and writing as it relates to communication design, and we welcome non-traditional work and work by emerging scholars. We also know that many of our number work outside of academia, and welcome experience reports that summarize important technologies, techniques, methods, pedagogies, or product processes. We are also interested in proposals for guest editing special issues.

And if you have something that would be a good fit for Communication Design Quarterly, please review our article guidelines<http://sigdoc.acm.org/publication/communication-design-quarterly-review/> (sigdoc.acm.org/publication/communication-design-quarterly-review/) and direct inquiries or submissions to our Editor in Chief, Dr. Derek G. Ross, at derek.ross at auburn.edu<mailto:derek.ross at auburn.edu>.

Best,
Luke Thominet, PhD
Communications Manager
SIGDOC
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