Summarizing Dialogic Arguments from Social Media

Misra, Amita; Oraby, Shereen; Tandon, Shubhangi; TS, Sharath; Anand, Pranav; Walker, Marilyn A.

doi:10.48550/arxiv.1711.00092

Cited by 2 publications

(2 citation statements)

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“…More fundamental examinations have made inquiries regarding how you can model persuasion and mine arguments from social media. (Dusmanu, Cabrio, and Villata 2017;Dutta, Das, and Chakraborty 2020;Misra et al 2017). Recent contributions have introduced argumentation datasets for further exploring new computational approaches (Bosc, Cabrio, and Villata 2016;Gretz et al 2020).…”

Section: Modelling Reasonability On Social Mediamentioning

confidence: 99%

Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole

Haworth

Grover

Langston

et al. 2021

ICWSM

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People regularly share retellings of their personal events through social media websites to elicit feedback about the reasonability of their actions in the event's context. In this paper, we explore how learning approaches can be used toward the goal of classifying reasonability in personal retellings of events shared on social media. We collect 13,748 community-labeled posts from /r/AmITheAsshole, a subreddit in which Reddit users share retellings of personal events which are voted upon by community members. We build and evaluate a total of 21 machine learning models across seven types of models and three distinct feature sets. We find that our best-performing model can predict the reasonability of a post with an F1 score of .76. Our findings suggest that features derived from the post and author metadata were more predictive than simple linguistic features like the post sentiment and types of words used. We conclude with a discussion on the implications of our findings as they relate to sharing retellings of personal events on social media and beyond.

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Section: Modelling Reasonability On Social Mediamentioning

confidence: 99%

Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole

Haworth

Grover

Langston

et al. 2021

ICWSM

View full text Add to dashboard Cite

show abstract

“…So, an analysis based on information from Twitter with a variety of computational linguistic methods allows users to find out the feelings of people about to have a child, how they plan to have children [15]. The sentiment analysis on demographic issues includes abortion [16][17][18][19][20][21], in particular, the legalization of abortion [22], various aspects of parenthood [14,15], health issues [5,21,23,24], drivers of demographic processes (e.g., natural disasters [25]), the demographic structure and trend of telemedicine [26], and the COVID-19 pandemic and other infections [27][28][29][30][31][32]. Topics closely related to demographics were also studied by sentiment analysis: these include sexual harassment or violence [33][34][35], attitudes towards genetic testing [36], processes of racial segregation [37].…”

Section: Introduction and Brief Literature Reviewmentioning

confidence: 99%

Text as Data in Demography: Russian-language experience

Kalabikhina,

Loukachevitch,

Banin

et al. 2024

Population and Development in the 21st Century - Between the Anthropocene and Anthropocentrism

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We propose to consider our experience in data use of Russian-language texts of social networks, electronic media, and search engines in demographic analysis. Experiments on the automatic classification of opinions have been carried out. Conversational RuBERT has been used in most cases. The following main scientific results on text data will be described: (1) short-term forecasts of fertility dynamics according to Google trend data, (2) automatic measurement of the demographic temperature of various demographic groups (pronatalists and antinatalists) in social networks, (3) sentiment analysis of reproductive behavior, sentiment analysis of vital behavior in pandemic, sentiment analysis of attitudes toward demographic and epidemiological policy according to social network data, (4) analysis of the arguments of social network users, and (5) analysis of media publications on demographic policy. A description of the created open databases of all these studies will be provided. All of the studies described will contain reflections on the advantages and difficulties of using texts as data in demographic analysis.

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Summarizing Dialogic Arguments from Social Media

Cited by 2 publications

References 0 publications

Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole

Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole

Text as Data in Demography: Russian-language experience

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