Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations

Lipizzi, Carlo; Dessavre, Dante Gama; Iandoli, Luca; Marquez, Jose Emmanuel Ramirez

doi:10.1016/j.chb.2016.07.030

Cited by 25 publications

(14 citation statements)

References 30 publications

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“…Bouvier, 2015) and hone in on content generation in real-time (cf. Bouvier, 2015; Lipizzi et al., 2016). Brock (2012), for example, considers ‘‘Black Twitter” as a mediator of Black cultural discourse.…”

Section: Social Media Geographiesmentioning

confidence: 99%

The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

Walker

Boamah²

2020

Big Data & Society

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The Central American migrant caravans of 2018 are best understood as having been precipitated by entangled multi-scalar geopolitical histories among the United States, Mexico, Guatemala, Honduras, and El Salvador. Unsurprisingly, the migrants traveling north to the United States garnered widespread attention on social media. So much so that the reaction to the caravan accelerated plans to deploy troops to the US southern border and deny Central Americans the opportunity to seek asylum. This example showcases how the digital world can have exponential material effects. While coverage on border security and migration has been extensive, within political geography, such concerns have rarely been paired with social media. In this article, we take as our object of analysis the digitality or “digital life” of the migrant caravan. Mapping the patterns of migrant caravan-related tweeting paired with the exploration of Twitter’s networked dimensions reveals the platform to be a fundamentally spatial technology. Rather than reflect, refract or distort, Twitter produces and (its power) is in turn produced through spatial mechanisms. We present multiple cartographic visualizations in support of this claim and highlight the ways in which a contextual knowledge of the subject under study—the migrant caravan—can further inform analyses of Big Data.

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Section: Social Media Geographiesmentioning

confidence: 99%

The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

Walker

Boamah²

2020

Big Data & Society

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“…Considerable research has increasingly demonstrated the effectiveness of using social media to study public health crises. With regards to the diverse methodologies used to mine social media text, many researchers used NLP-based approaches such as discourse analysis [1], sentiment classification [2], and topic modeling. Social media text mining was utilized to study public health crises related to infectious diseases such as Ebola and Zika.…”

Section: Related Workmentioning

confidence: 99%

“…The COVID-19 disease has led to a global pandemic and the latest global public health crisis. At the time of writing this paper, global cases have surpassed 172 million (over 33 million US cases), and there are more than 3 million deaths worldwide (over 597 thousand US deaths) 1 . As a result, governments worldwide have developed new policies and mandates to help mitigate the spread of the disease.…”

Section: Introductionmentioning

confidence: 99%

Surveillance of COVID-19 Pandemic using Social Media: A Reddit Study in North Carolina

Whitfield,

Liu,

Anwar

2021

Preprint

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Coronavirus disease (COVID-19) pandemic has changed various aspects of people's lives and behaviors. At this stage, there are no other ways to control the natural progression of the disease than adopting mitigation strategies such as wearing masks, watching distance, and washing hands. Moreover, at this time of social distancing, social media plays a key role in connecting people and providing a platform for expressing their feelings. In this study, we tap into social media to surveil the uptake of mitigation and detection strategies, and capture issues and concerns about the pandemic. In particular, we explore the research question, "how much can be learned regarding the public uptake of mitigation strategies and concerns about COVID-19 pandemic by using natural language processing on Reddit posts?" After extracting COVID-related posts from the four largest subreddit communities of North Carolina over six months, we performed NLP-based preprocessing to clean the noisy data. We employed a custom Named-entity Recognition (NER) system and a Latent Dirichlet Allocation (LDA) method for topic modeling on a Reddit corpus. We observed that mask, flu, and testing are the most prevalent named-entities for "Personal Protective Equipment", "symptoms", and "testing" categories, respectively. We also observed that the most discussed topics are related to testing, masks, and employment. The mitigation measures are the most prevalent theme of discussion across all subreddits.

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“…In tf-idf, a widely used method for extracting new topics, the usage-frequency of a word in combination with the inverse-frequency of documents including the word define the relevance between the topic and a document. In social networks, a user may apply unconventional wordings, phrases, hashtags and abbreviations to efficiently communicate her message, thereby, a networked model of terms and words are applied to reconstruct grammar in analysis of discourse Himelboim et al (2017), Lipizzi et al (2016). In this chapter, I also opt to employ the networked structure of language for clustering viral topics.…”

Section: Clustering Modelmentioning

confidence: 99%

“…al. used a graph-based approach using adjacency matrix of concatenation among keywords to identify real-world discourses expressed through backchanneling on social networks Lipizzi et al (2016) where a similar approach can cluster users based on trending topics Hachaj and Ogiela (2017). Xie and Mathioudakis employ the concepts of popular and bursty keywords to detect topics in real-time Xie et al (2016), Mathioudakis and Koudas (2010).…”

Section: Introductionmentioning

confidence: 99%

An Influence-based Clustering Model on Twitter

Ehsanfar,

Mansouri

2018

Preprint

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This paper introduces a temporal framework for detecting and clustering emergent and viral topics on social networks. Endogenous and exogenous influence on developing viral content is explored using a clustering method based on the a user's behavior on social network and a dataset from Twitter API. Results are discussed by introducing metrics such as popularity, burstiness, and relevance score. The results show clear distinction in characteristics of developed content by the two classes of users.

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Towards computational discourse analysis: A methodology for mining Twitter backchanneling conversations

Cited by 25 publications

References 30 publications

The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology

Surveillance of COVID-19 Pandemic using Social Media: A Reddit Study in North Carolina

An Influence-based Clustering Model on Twitter

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