Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning

Ning, Yue; Muthiah, Sathappan; Rangwala, Huzefa; Ramakrishnan, Naren

doi:10.1145/2939672.2939802

Cited by 49 publications

(27 citation statements)

References 23 publications

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“…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. Ning et al [26] proposed a multiple instance learning based approach that jointly forecasts protest events and identifies event precursors from news articles. Ramakrishnan et al [27] proposed to forecast civil unrest from multiple data sources using models such as logistic regression with Lasso.…”

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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“…The authors of [17] have developed a method that solves the problem of identifying precursors and predicting future events. According to data from the collection of streaming news (news taken from several open sources of three Latin American countries), a nested approach was developed to predict significant public events and protests.…”

Section: Review Of Research On Forecasting Events Based On Text Analysismentioning

confidence: 99%

Stochastic Diffusion Model for Analysis of Dynamics and Forecasting Events in News Feeds

2021

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One of the problems of forecasting events in news feeds, is the development of models which allow for work with semi structured information space of text documents. This article describes a model for forecasting events in news feeds, which is based on the use of stochastic dynamics of changes in the structure of non-stationary time series in news clusters (states of the information space) on the basis of use of diffusion approximation. Forecasting events in a news feed is based on their text description, vectorization, and finding the cosine value of the angle between the given vector and the centroids of various information space semantic clusters. Changes over time in the cosine value of such angles between the above vector and centroids can be represented as a point wandering on the [0, 1] segment. This segment contains a trap at the event occurrence threshold point, which the wandering point may eventually fall into. When creating the model, we have considered probability patterns of transitions between states in the information space. On the basis of this approach, we have derived a nonlinear second-order differential equation; formulated and solved the boundary value problem of forecasting news events, which allowed obtaining theoretical time dependence on the probability density function of the parameter distribution of non-stationary time series, which describe the information space evolution. The results of simulating the events instance probability dependence on time (with sets of parameter values of the developed model, which have been experimentally determined for already occurred events) show that the model is consistent and adequate (all the news events which have been used for the model verification occur with high values of probability (within the order of 80%), or if these are fictitious events, they can only occur over the course of inadmissible long time).

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“…They used as a set of news articles to develop a nested multiple instance learning model to predict events across multiple countries. This model can identify the news articles that can be used as precursors for a protest [26].…”

Section: A Keyword-based Approachesmentioning

confidence: 99%

Event Detection in Twitter: A Keyword Volume Approach

Hossny

Mitchell

2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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Event detection using social media streams needs a set of informative features with strong signals that need minimal preprocessing and are highly associated with events of interest. Identifying these informative features as keywords from Twitter is challenging, as people use informal language to express their thoughts and feelings. This informality includes acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, we propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests. The volume of these keywords is tracked in real time to identify the events of interest in a binary classification scheme. We use keywords within wordpairs to capture the context. The proposed method is to binarize vectors of daily counts for each word-pair by applying a spike detection temporal filter, then use the Jaccard metric to measure the similarity of the binary vector for each word-pair with the binary vector describing event occurrence. The top n wordpairs are used as features to classify any day to be an event or non-event day. The selected features are tested using multiple classifiers such as Naive Bayes, SVM, Logistic Regression, KNN and decision trees. They all produced AUC ROC scores up to 0.91 and F1 scores up to 0.79. The experiment is performed using the English language in multiple cities such as Melbourne, Sydney and Brisbane as well as the Indonesian language in Jakarta. The two experiments, comprising different languages and locations, yielded similar results.

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Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning

Cited by 49 publications

References 23 publications

Activism via attention: interpretable spatiotemporal learning to forecast protest activities

Activism via attention: interpretable spatiotemporal learning to forecast protest activities

Stochastic Diffusion Model for Analysis of Dynamics and Forecasting Events in News Feeds

Event Detection in Twitter: A Keyword Volume Approach

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