On predicting social unrest using social media

Korolov, Rostyslav; Lu, Di; Wang, Jingjing; Zhou, Guangyu; Bonial, Claire; Voss, Clare R.; Kaplan, Lance; Wallace, William A.; Ji, Heng

doi:10.1109/asonam.2016.7752218

Cited by 48 publications

(36 citation statements)

References 26 publications

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“…There have been studies that utilize the spatial, temporal or spatiotemporal dependencies in modeling or predicting the events. Several studies employed logistic regression or heuristics to forecast/detect events from social media related to anomalies [20,21], crime [22] and civil unrest [23,24]. Cadena et al [25] proposed an event forecasting model for civil unrest that uses a notion of activity cascades derived from the Twitter communication networks.…”

Section: Forecasting Protests and Other Eventsmentioning

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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Section: Forecasting Protests and Other Eventsmentioning

confidence: 99%

Activism via attention: interpretable spatiotemporal learning to forecast protest activities

et al. 2019

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“…This excerpt of some of the relations to PROTEST captures social science theories suggesting that a protest is generally mobilized where there is a sense of a group identity and a grievance or trigger for intergroup conflict, and that protest by nature involves the communication of some claims calling for change. The event structure found in the ontology for PROTEST parallels the "stages" of protest outlined in Korolov et al (2016), who find that trigger words associated with these stages can be used to predict social protest based on social media messaging. REO users can take advantage of ontological relations in their queries.…”

Section: Querying: From Events To Scenariosmentioning

confidence: 78%

The Rich Event Ontology

Brown¹,

Bonial²,

Obrst³

et al. 2017

Proceedings of the Events and Stories in the News Workshop

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In this paper we describe a new lexical semantic resource, The Rich Event Ontology, which provides an independent conceptual backbone to unify existing semantic role labeling (SRL) schemas and augment them with event-to-event causal and temporal relations. By unifying the FrameNet, VerbNet, Automatic Content Extraction, and Rich Entities, Relations and Events resources, the ontology serves as a shared hub for the disparate annotation schemas and therefore enables the combination of SRL training data into a larger, more diverse corpus. By adding temporal and causal relational information not found in any of the independent resources, the ontology facilitates reasoning on and across documents, revealing relationships between events that come together in temporal and causal chains to build more complex scenarios. We envision the open resource serving as a valuable tool for both moving from the ontology to text to query for event types and scenarios of interest, and for moving from text to the ontology to access interpretations of events using the combined semantic information housed there.

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“…Monitoring and comprehending the deliberated topics is an essential element of each trustworthy modern multimedia system. Therefore, the study of social network chatting is a useful forecasting tool in many areas, such as election results [ 1 , 2 ], criminal activity [ 3 , 4 ], and social events [ 5 , 6 , 7 ]. This kind of analysis sensibly summarizes the feelings and aims revealed in social networks without any relation to texts’ linguistic content, while it is generally an outcome of the collective creativity uncovered by an informal language in a faithful matter.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Approach for the Detection of Changes in Arabic Newspapers’ Content

Bernikova

Granichin

Lemberg

et al. 2020

Entropy

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A new method for the recognition of meaningful changes in social state based on transformations of the linguistic content in Arabic newspapers is suggested. The detected alterations of the linguistic material in Arabic newspapers play an indicator role. The currently proposed approach acts in an “online” fashion and uses pre-trained vector representations of Arabic words. After a pre-processing stage, the words in the issues’ texts are substituted by vectors obtained within a word embedding methodology. The approach typifies the consistent linguistic template by the similarity of the embedded vectors. A change in the distributions of the issue-grounded samples indicates a difference in the underlying newspaper template. A two-step procedure implements the concept, where the first step compares the similarity distribution of the current issue versus the union of ones corresponding to several of its predecessors. A repeating under-sampling approach accompanied by a two-sample test stabilizes the sampling and returns a collection of the resultant p-values. In the second stage, the entropy of these sets is sequentially calculated, such that the change points of the time series obtained in this way indicate the changes in the newspaper content. Numerical experiments provided on the following issues of several Arabic newspapers published in the Arab Spring period demonstrate the high reliability of the method.

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On predicting social unrest using social media

Cited by 48 publications

References 26 publications

Activism via attention: interpretable spatiotemporal learning to forecast protest activities

Activism via attention: interpretable spatiotemporal learning to forecast protest activities

The Rich Event Ontology

Entropy-Based Approach for the Detection of Changes in Arabic Newspapers’ Content

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