Probabilistic Model of Narratives Over Topical Trends in Social Media

Oghaz, Toktam A.; Mutlu, Ece C.; Jasser, Jasser; Yousefi, Niloofar; Garibay, Iván

doi:10.1145/3372923.3404790

Cited by 10 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the COVID-CQ data set is focused on a month-length Twitter activities regarding the COVID-19 pandemic ( [7] , and particularly, on the chloroquine/hydroxychloroquine conversations, this data contains textual content in relation to many major events that occurred since the beginning of the pandemic up to the end of April 2020. According to narrative summaries representing the ordered chain of individual events ( [6] ), below we briefly narrate the major events that appear in COVID-CQ data set. However, this narration does not reflect authors’ personal opinions towards any of the reviewed events.…”

Section: Major Events and Narrativesmentioning

confidence: 99%

A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19

et al. 2020

Self Cite

View full text Add to dashboard Cite

At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, especially the Twitter platform, is experiencing a high volume of activity related to this pandemic. Among the hot topics, the polarized debates about unconfirmed medicines for the treatment and prevention of the disease have attracted significant attention from online media users. In this work, we present a stance data set, COVID-CQ, of user-generated content on Twitter in the context of COVID-19. We investigated more than 14 thousand tweets and manually annotated the tweet initiators’ opinions regarding the use of “chloroquine” and “hydroxychloroquine” for the treatment or prevention of COVID-19. To the best of our knowledge, COVID-CQ is the first data set of Twitter users’ stances in the context of the COVID-19 pandemic, and the largest Twitter data set on users’ stances towards a claim, in any domain. We have made this data set available to the research community via the Mendeley Data repository. We expect this data set to be useful for many research purposes, including stance detection, evolution and dynamics of opinions regarding this outbreak, and changes in opinions in response to the exogenous shocks such as policy decisions and events.

show abstract

Section: Major Events and Narrativesmentioning

confidence: 99%

A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Brexit referendum is an important case of how online social networks can have a pivotal effect in determining the outcome of a major polarized political discussion [19]. Among online social network platforms, given its multi-directional interactivity and its popularity among politicians and journalists, Twitter is considered one of the most important ones [19,30]. The conversations of Brexit on Twitter provide an apt case study to investigate polarization in the context of online social networks, given its dichotomous nature of discussion and outcome.…”

Section: Introductionmentioning

confidence: 99%

Exploring the disparity of influence between users in the discussion of Brexit on Twitter

et al. 2021

Self Cite

View full text Add to dashboard Cite

The topic of political polarization has received increased attention for valid reasons. Given that an increased amount of the social exchange for opinions happens online, social media platforms provide a good source of information to investigate various aspects of the phenomena. In this work, data collected from Twitter are used to examine polarization surrounding the topic of the Brexit referendum on the membership of the European Union. The analysis specifically focuses on the question of how different tiers of users in terms of influence can project their opinions and if the polarized conditions affect the relative balance in the broadcast capabilities of the tiers. The results show that during polarization periods, users of the higher tier have increased capabilities to broadcast their information in relation to the lower tiers thereby further dominating the discussion. This validates previous modeling investigations and the hypothesis that polarization provides an opportunity for influencers to increase their relative social capital.

show abstract

On Countering Disinformation with Caution: Effective Inoculation Strategies and Others that Backfire into Community Hyper-Polarization

Rajabi

Gunaratne

Mantzaris

et al. 2020

Social, Cultural, and Behavioral Modeling

View full text Add to dashboard Cite

Probabilistic Model of Narratives Over Topical Trends in Social Media

Cited by 10 publications

References 26 publications

A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19

A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19

Exploring the disparity of influence between users in the discussion of Brexit on Twitter

On Countering Disinformation with Caution: Effective Inoculation Strategies and Others that Backfire into Community Hyper-Polarization

Contact Info

Product

Resources

About