Uncovering Social Media Reaction Pattern to Protest Events: A Spatiotemporal Dynamics Perspective of Ferguson Unrest

He, Jiaying; Hong, Lingzi; Frías-Martínez, Vanessa; Torrens, Paul M.

doi:10.1007/978-3-319-27433-1_5

Cited by 15 publications

(15 citation statements)

References 13 publications

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“…While most of the users act as passive listeners or broadcasters, Twitter conversations are usually driven by a small elite of influential accounts [5,38]. The composition of such elite varies depending on the main subject of the conversation and on the interaction medium [33].…”

Section: Communication Among the Elitesmentioning

confidence: 99%

Recurrent Patterns of User Behavior in Different Electoral Campaigns: A Twitter Analysis of the Spanish General Elections of 2015 and 2016

2018

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We have retrieved and analyzed several millions of Twitter messages corresponding to the Spanish general elections held on the 20th of December 2015 and repeated on the 26th of June 2016. The availability of data from two electoral campaigns that are very close in time allows us to compare collective behaviors of two analogous social systems with a similar context. By computing and analyzing the time series of daily activity, we have found a significant linear correlation between both elections. Additionally, we have revealed that the daily number of tweets, retweets, and mentions follow a power law with respect to the number of unique users that take part in the conversation. Furthermore, we have verified that the topologies of the networks of mentions and retweets do not change from one election to the other, indicating that their underlying dynamics are robust in the face of a change in social context. Hence, in the light of our results, there are several recurrent collective behavioral patterns that exhibit similar and consistent properties in different electoral campaigns.

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Section: Communication Among the Elitesmentioning

confidence: 99%

Recurrent Patterns of User Behavior in Different Electoral Campaigns: A Twitter Analysis of the Spanish General Elections of 2015 and 2016

2018

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“…In other words, social movements are not merely instances of independent collective actions or protest events, but need to be investigated within their social, temporal and geographical contexts [1]. Empirically, however, in part due to the lack of proper analytical tools, studies (including social media studies) often analyze single events or movements via a case-study approach [7][8][9][10], or consider a large number of movement-related events independently of their relationships in time and space [11,12].…”

Section: Introductionmentioning

confidence: 99%

Activism via attention: interpretable spatiotemporal learning to forecast protest activities

et al. 2019

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The diffusion of new information and communication technologies-social media in particular-has played a key role in social and political activism in recent decades. In this paper, we propose a theory-motivated, spatiotemporal learning approach, ActAttn, that leverages social movement theories and a deep learning framework to examine the relationship between protest events and their social and geographical contexts as reflected in social media discussions. To do so, we introduce a novel predictive framework that incorporates a new design of attentional networks, and which effectively learns the spatiotemporal structure of features. Our approach is not only capable of forecasting the occurrence of future protests, but also provides theory-relevant interpretations-it allows for interpreting what features, from which places, have significant contributions on the protest forecasting model, as well as how they make those contributions. Our experiment results from three movement events indicate that ActAttn achieves superior forecasting performance, with interesting comparisons across the three events that provide insights into these recent movements.

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“…Related work on reactions to events expressed in social media can be differentiated by the category of the event the reaction is related to and by the purpose of the study. Event categories include natural disasters (Hashimoto et al 2013), speeches (Amanatullah et al 2013), health-related events (Szomszor et al 2011, Fung et al 2015, Nikfarjam et al 2015, Meaney et al 2016, advertising campaigns (Rodrigues 2016), criminal and terrorist events (Burnap et al 2014, Kounadi et al 2015McEnery et al 2015), protests or unrest (He et al 2015), and entertainment-related events (Lipizzi et al 2016). The purpose of such studies includes investigating the diffusion of reactions (Burnap et al 2014), analysing perception of events (i.e.…”

Section: Reactions To Events In Lbsmmentioning

confidence: 99%

“…The purpose of such studies includes investigating the diffusion of reactions (Burnap et al 2014), analysing perception of events (i.e. the attitudes and concerns triggered by an event) (Hashimoto et al 2013, He et al 2015, Kounadi et al 2015, Meaney et al 2016, identifying trusted or credible information sources (Szomszor et al 2011), event detection from reactions including monitoring (Amanatullah et al 2013, Nikfarjam et al 2015, assessment of the effectiveness of advertising campaigns (Rodrigues 2016), sales prediction (Lipizzi et al 2016) or interrelationships with news media (Castillo et al 2014, Tsytsarau et al 2014, Fung et al 2015, McEnery et al 2015. Importantly, the last topic explicitly makes the link between another mediumthe influence of the press on the reactions to an event, making clear that social media reactions cannot be considered as purely a function of a given event, but rather a discourse conducted through a multitude of media around an event.…”

Section: Reactions To Events In Lbsmmentioning

confidence: 99%

A conceptual framework for studying collective reactions to events in location-based social media

Dunkel

Andrienko

et al. 2018

International Journal of Geographical Information Science

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Events are a core concept of spatial information, but locationbased social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events.

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Uncovering Social Media Reaction Pattern to Protest Events: A Spatiotemporal Dynamics Perspective of Ferguson Unrest

Cited by 15 publications

References 13 publications

Recurrent Patterns of User Behavior in Different Electoral Campaigns: A Twitter Analysis of the Spanish General Elections of 2015 and 2016

Recurrent Patterns of User Behavior in Different Electoral Campaigns: A Twitter Analysis of the Spanish General Elections of 2015 and 2016

Activism via attention: interpretable spatiotemporal learning to forecast protest activities

A conceptual framework for studying collective reactions to events in location-based social media

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