Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

Łukasik, Michał; Srijith, P. K.; Vu, Duy; Bontcheva, Kalina; Zubiaga, Arkaitz; Cohn, Trevor

doi:10.18653/v1/p16-2064

Cited by 112 publications

(113 citation statements)

References 11 publications

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“…Prior work on stance classification investigated various features varying from syntactical, semantical, indicator, user-specific, message-specific, etc. types [16,18,7,13,21,15,23]. This paper adopts the features from these papers, coupled with experiments with a wide range of machine learning classifiers.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Stance Classification in Out-of-Domain Rumours: A Case Study Around Mental Health Disorders

Aker

Zubiaga

Bontcheva

et al. 2017

Lecture Notes in Computer Science

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Abstract. Social media being a prolific source of rumours, stance classification of individual posts towards rumours has gained attention in the past few years. Classification of stance in individual posts can then be useful to determine the veracity of a rumour. Research in this direction has looked at rumours in different domains, such as politics, natural disasters or terrorist attacks. However, work has been limited to in-domain experiments, i.e. training and testing data belong to the same domain. This presents the caveat that when one wants to deal with rumours in domains that are more obscure, training data tends to be scarce. This is the case of mental health disorders, which we explore here. Having annotated collections of tweets around rumours emerged in the context of breaking news, we study the performance stability when switching to the new domain of mental health disorders. Our study confirms that performance drops when we apply our trained model on a new domain, emphasising the differences in rumours across domains. We overcome this issue by using a little portion of the target domain data for training, which leads to a substantial boost in performance. We also release the new dataset with mental health rumours annotated for stance.

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Section: Methodsmentioning

confidence: 99%

“…Previous research on rumour stance classification for tweets has mostly focused on rumours about politics, natural disasters or terrorist attacks [16,18,7,13,21,15,23]. The fact that it is relatively easy to retrieve large amounts of data for these domains has enabled them to use in-domain data for training.…”

Section: Introductionmentioning

confidence: 99%

Stance Classification in Out-of-Domain Rumours: A Case Study Around Mental Health Disorders

Aker

Zubiaga

Bontcheva

et al. 2017

Lecture Notes in Computer Science

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“…Stance detection and opinion mining is closely related to conflict identification and measurement. Most of the previous works in stance detection are based on stance classification of rumors in Twitter [27,29,42]. Rosenthal and McKewon [33] propsed a agreement-disagreement identification framework for discussions in Create Debate and Wikipedia Talkpages.…”

Section: Related Workmentioning

confidence: 99%

Into the Battlefield

Dutta

Das

Kaur

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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Over the last decade, online forums have become primary news sources for readers around the globe, and social media platforms are the space where these news forums find most of their audience and engagement. Our particular focus in this paper is to study conflict dynamics over online news articles in Reddit, one of the most popular online discussion platforms. We choose to study how conflicts develop around news inside a discussion community, the r/news subreddit. Mining the characteristics of these engagements often provide useful insights into the behavioral dynamics of largescale human interactions. Such insights are useful for many reasons -for news houses to improvise their publishing strategies and potential audience, for data analytics to get a better introspection over media engagement as well as for social media platforms to avoid unnecessary and perilous conflicts.In this work, we present a novel quantification of conflict in online discussion. Unlike previous studies on conflict dynamics, which model conflict as a binary phenomenon, our measure is continuous-valued, which we validate with manually annotated ratings. We address a two-way prediction task. Firstly, we predict the probable degree of conflict a news article will face from its audience. We employ multiple machine learning frameworks for this task using various features extracted from news articles. Secondly, given a pair of users and their interaction history, we predict if their future engagement will result in a conflict. We fuse textual and network-based features together using a support vector machine which achieves an AUC of 0.89. Moreover, we implement a graph convolutional model which exploits engagement histories of users to predict whether a pair of users who never met each other before will have a conflicting interaction, with an AUC of 0.69.We perform our studies on a massive discussion dataset crawled from the Reddit news community, containing over 41k news articles and 5.5 million comments. Apart from the prediction tasks, our studies offer interesting insights on the conflict dynamics -how users form clusters based on conflicting engagements, how different is the temporal nature of conflict over different online news forums, how is contribution of different language based features to induce conflict, etc. In short, our study paves the way towards new methods of exploration and modeling of conflict dynamics inside online discussion communities.

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“…The use of sequential classifiers to model the conversational properties inherent in social media threads is still in its infancy. For example, in preliminary work we showed that a sequential classifier modelling the temporal sequence of tweets outperforms standard classifiers [18,19]. Here we extend this preliminary experimentation in four different directions that enable exploring further the stance classification task using sequential classifiers: (1) we perform a comparison of a range of sequential classifiers, including a Hawkes Process classifier, a Linear CRF, a Tree CRF and an LSTM; (2) departing from the use of only local features in our previous work, we also test the utility of contextual features to model the conversational structure of Twitter threads; (3) we perform a more exhaustive analysis of the results looking into the impact of different datasets and the depth of the replies in the conversations on the classifiers' performance, as well as performing an error analysis; and (4) we perform an analysis of features that gives insight into what characterises the different kinds of stances observed around rumours in social media.…”

Section: Introductionmentioning

confidence: 99%

“…• We perform an analysis of whether and the extent to which use of the sequential structure of conversational threads can improve stance classification in comparison to a classifier that determines a tweet's stance from the tweet in isolation. To do so, we evaluate the effectiveness of a range of sequential classifiers: (1) a state-of-the-art classifier that uses Hawkes Processes to model the temporal sequence of tweets [18]; (2) two different variants of Conditional Random Fields (CRF), i.e., a linear-chain CRF and a tree CRF; and (3) a classifier based on Long Short Term Memory (LSTM) networks. We compare the performance of these sequential classifiers with non-sequential baselines, including the non-sequential equivalent of CRF, a Maximum Entropy classifier.…”

Section: Introductionmentioning

confidence: 99%

Discourse-aware rumour stance classification in social media using sequential classifiers

Zubiaga

Kochkina

Liakata

et al. 2018

Information Processing & Management

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117

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Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest to researchers. While most previous work has focused on using individual tweets as classifier inputs, here we report on the performance of sequential classifiers that exploit the discourse features inherent in social media interactions or 'conversational threads'. Testing the effectiveness of four sequential classifiers -Hawkes Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM) -on eight datasets associated with breaking news stories, and looking at different types of local and contextual features, our work sheds new light on the development of accurate stance classifiers. We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers. Furthermore, we show that LSTM using a reduced set of features can outperform the other sequential classifiers; this performance is consistent across datasets and across types of stances. To conclude, our work also analyses the different features under study, identifying those that best help characterise and distinguish between stances, such as supporting tweets being more likely to be accompanied by evidence than denying tweets. We also set forth a number of directions for future research.

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Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

Cited by 112 publications

References 11 publications

Stance Classification in Out-of-Domain Rumours: A Case Study Around Mental Health Disorders

Stance Classification in Out-of-Domain Rumours: A Case Study Around Mental Health Disorders

Into the Battlefield

Discourse-aware rumour stance classification in social media using sequential classifiers

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