Chapter 4. A preliminary typology of interactional figures based on a tool for visualizing conversational structure

Author(s):  
Guadalupe Espinosa-Guerri ◽  
Amparo García-Ramón
2020 ◽  
Vol 14 (02) ◽  
pp. 273-293
Author(s):  
Yingcheng Sun ◽  
Richard Kolacinski ◽  
Kenneth Loparo

With the explosive growth of online discussions published everyday on social media platforms, comprehension and discovery of the most popular topics have become a challenging problem. Conventional topic models have had limited success in online discussions because the corpus is extremely sparse and noisy. To overcome their limitations, we use the discussion thread tree structure and propose a “popularity” metric to quantify the number of replies to a comment to extend the frequency of word occurrences, and the “transitivity” concept to characterize topic dependency among nodes in a nested discussion thread. We build a Conversational Structure Aware Topic Model (CSATM) based on popularity and transitivity to infer topics and their assignments to comments. Experiments on real forum datasets are used to demonstrate improved performance for topic extraction with six different measurements of coherence and impressive accuracy for topic assignments.


2018 ◽  
Vol 88 (1) ◽  
Author(s):  
Jörg Peters

This paper presents an outline of an autosegmental-metrical analysis of German intonation adopting Gussenhoven’s (1983, 2005) approach to Dutch intonation. A features-based interpretation of the phonological units is given, which is based on an analysis of tonal contrasts. This analysis suggests that tones of different tone classes bear semantic features that relate to the mutual belief space, information packaging, conversational structure, thematic structure, conceptual structure, and speaker attitudes.


Author(s):  
David Manheim ◽  
Anat Gesser-Edelsburg

Abstract This paper considers how health education organizations in the World Health Organization's Vaccine Safety Network (VSN) use Twitter to communicate about vaccines with the public, and whether they answer questions and engage in conversations. Almost no research in public health, to our knowledge, has explored conversational structure on social media among posts sent by different accounts. Starting with 1,017,176 tweets by relevant users, we constructed two corpuses of multi-tweet conversations. The first was 1,814 conversations that included VSN members directly, while the second was 2,283 conversations mentioning vaccines or vaccine denialism. The tweets and user metadata was then analyzed using an adaptation of Rhetorical Structure Theory. In the studied data, VSN members tweeted 12,677 times within conversations, compared to their 37,587 lone tweets. Their conversations were shorter than those in the comparison corpus (P < 0.0001), and they were involved in fewer multilogues (P < 0.0001). We also see that while there is diversity among organizations, most were tied to the pre-social-media broadcast model. In the future, they should try to converse more, rather than tweet more, and embrace best-practices in risk-communication.


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