Hashtag Popularity on Twitter: Analyzing Co-occurrence of Multiple Hashtags

Pervin, Nargis; Phan, Tuan Q.; Datta, Anindya; Takeda, Hideaki; Toriumi, Fujio

doi:10.1007/978-3-319-20367-6_18

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…At the same time, we are surprised to see that this indexing function does not drive engagement. Contrary to previous research 49 , we conclude that hashtags are negatively correlated with retweet count and serve a primarily affiliative function-at least as they are used in connection with the Black Lives Matter movement.…”

Section: Discussioncontrasting

confidence: 99%

The affiliative use of emoji and hashtags in the Black Lives Matter movement: A Twitter case study

Alfano

Reimann

Quintana

et al. 2021

Preprint

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Protests and counter-protests seek to draw and direct attention and concern with confronting images and slogans. In recent years, as protests and counter-protests have partially migrated to the digital space, such images and slogans have also gone online. Two main ways in which these images and slogans are translated to the online space is through the use of emoji and hashtags. Despite sustained academic interest in online protests, hashtag activism and the use of emoji across social media platforms, little is known about the specific functional role that emoji and hashtags play in online social movements. In an effort to fill this gap, the current paper studies both hashtags and emoji in the context of the Twitter discourse around the Black Lives Matter movement.

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Section: Discussioncontrasting

confidence: 99%

The affiliative use of emoji and hashtags in the Black Lives Matter movement: A Twitter case study

Alfano

Reimann

Quintana

et al. 2021

Preprint

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“…Co-occurrence analysis can be used to construct networks (i.e., graphs) representing the connection between words, revealing thematic clusters (Buzydlowski, 2015). As a general method in content analysis, co-occurrence was used even before the introduction of computational methods (Harris, 1957) and has been used specifically for the analysis of social media data in numerous studies (e.g., Aiello et al, 2013;Pervin, Phan, Datta, Takeda, & Toriumi, 2015;Wang, Wei, Liu, Zhou, & Zhang, 2011). In the current study, we used cooccurrence graphs to gain insights into the use of hashtags within the debate.…”

Section: Co-occurrence Graphsmentioning

confidence: 99%

A Computational Approach to Analyzing the Twitter Debate on Gaming Disorder

Schatto‐Eckrodt

Janzik

Reer

et al. 2020

MaC

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The recognition of excessive forms of media entertainment use (such as uncontrolled video gaming or the use of social networking sites) as a disorder is a topic widely discussed among scientists and therapists, but also among politicians, journalists, users, and the industry. In 2018, when the World Health Organization (WHO) decided to include the addictive use of digital games (gaming disorder) as a diagnosis in the International Classification of Diseases, the debate reached a new peak. In the current article, we aim to provide insights into the public debate on gaming disorder by examining data from Twitter for 11 months prior to and 8 months after the WHO decision, analyzing the (change in) topics, actors, and sentiment over time. Automated content analysis revealed that the debate is organic and not driven by spam accounts or other overly active ‘power users.’ The WHO announcement had a major impact on the debate, moving it away from the topics of parenting and child welfare, largely by activating actors from gaming culture. The WHO decision also resulted in a major backlash, increasing negative sentiments within the debate.

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“…To achieve this, binary classifiers (burst vs. non-burst) have been trained using content features from hashtags, users or tweets. These features can be the length of the hashtag [6,7,9,11], the number of involved Twitter users/mentions [6][7][8][9][10][11], the user's followee/follower network [6][7][8]10], or even the graph built from these given features [8][9][10]. Another important feature is time series.…”

Section: Related Workmentioning

confidence: 99%

“…Another important feature is time series. For instance, time-series vectors can be directly used as features for classification [6,10]). The notion of time has also been widely used in other tasks for modelling the temporal trends of tweets [15] and topic modelling [12].…”

Section: Related Workmentioning

confidence: 99%