Adaptive Representations for Tracking Breaking News on Twitter

Twitter is often the most up-to-date source for finding and tracking breaking news stories. Therefore, there is considerable interest in developing filters for tweet streams in order to track and summarize stories. This is a non-trivial text analytics task as tweets are short, and standard retrieval methods often fail as stories evolve over time. In this paper we examine the effectiveness of adaptive mechanisms for tracking and summarizing breaking news stories. We evaluate the effectiveness of these mechanisms on a number of recent news events for which manually curated timelines are available. Assessments based on ROUGE metrics indicate that an adaptive approaches are best suited for tracking evolving stories on Twitter.Comment: 8 Pag

A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a unified contrastive transfer framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. More specifically, we first represent rumor circulated on social media as an undirected topology, and then train a Multi-scale Graph Convolutional Network via a unified contrastive paradigm. Our model explicitly breaks the barriers of the domain and/or language issues, via language alignment and a novel domain-adaptive contrastive learning mechanism. To enhance the representation learning from a small set of target events, we reveal that rumor-indicative signal is closely correlated with the uniformity of the distribution of these events. We design a target-wise contrastive training mechanism with three data augmentation strategies, capable of unifying the representations by distinguishing target events. Extensive experiments conducted on four low-resource datasets collected from real-world microblog platforms demonstrate that our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.Comment: A significant extension of the first contrastive approach for low-resource rumor detection (arXiv:2204.08143

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

(Mal) adjustment to societal crisis: a case study from the analysis of coping expressions on social media

The present research had the goal to explore how individuals perceived, reacted to and coped with the Ebola virus outbreak in 2014, which was considered to be a health epidemic. When confronted with public health events perceived as threats, people tend to adapt to them by individually and collectively make sense of them (e.g. concerning the source of contagion) and manage resources to best cope with the demands posed. One of the maladaptive side-effects of this sense making process occurs when individuals associate the epidemic and its related features to specific social groups, for example by blaming them for the outbreak and ultimately, stigmatizing. In the specific case of the Ebola epidemic, we aimed to comprehend whether it was 1) more associated to the African continent and its related social groups (Africans; African countries; …) than to other countries, as evidence of a Symbolic Othering effect; and 2) if there were natural manifestations of this othering, by means of coping with the perceived threat, in the form of escape and opposition coping strategies.. Hence, we aimed to demonstrate the Symbolic Othering effect by means of a web-based questionnaire in which participants estimated the percentage of cases of human contamination, in non-contaminated African and non-African countries. Secondly, we aimed to present evidence of naturally occurring instances of Symbolic Othering in the form of coping expressions collected on social media, namely Twitter. This multi-method approach allowed both a qualitative and quantitative analysis. Results showed a strong association between the Ebola epidemic and the African continent, with more human contamination cases identified in African countries, even though they had an actual zero percentage of cases. Moreover, the qualitative analysis of twitter data showed direct and indirect mentions to the social group – Africa/Africans/African countries – in addition to the identification of other groups to blame for the epidemic and its social amplification, such as the government, media and other targets. Overall, these results present themselves as a relevant for health crisis managers and communicators, given that Symbolic Othering effects may be found when people perceived health related events as threats, which may eventually lead into social stigmatization processes.A presente investigação teve como objetivo explorar de que forma os indivíduos percepcionaram, reagiram e lidaram com o surto do vírus Ébola em 2014, o qual foi considerado uma epidemia de saúde. Quando confrontadas com eventos de saúde pública avaliados enquanto ameaças, as pessoas tendem adaptar-se às mesmas, de forma individual e coletiva, de modo a conferir-lhes um sentido (por exemplo, em relação à fonte de contágio) e gerir recursos para melhor lidar com as exigências. Um dos efeitos colaterais deste processo de procura de sentido é desadaptativo, dado que consiste em associar a epidemia e as suas características a grupos sociais específicos, por exemplo, culpando-os e, eventualmente, estigmatizando-os. No caso específico da epidemia do Ébola, o nosso objetivo foi compreender se: 1) esta estaria mais associada ao continente Africano/países africanos (em comparação a outros países), como evidência de um efeito de othering simbólico; e 2) se existiam expressões naturais deste othering, através de estratégias de enfrentamento como o escape e a oposição. Deste modo, procurámos demonstrar o efeito de othering simbólico através da aplicação de um questionário online, no qual os aprticipantes estimavam a percentagem de casos de contaminação humana em países africanos e não-africanos, todos não contaminados. Segundo, procurámos apresentar evidências de othering simbólico refletidas em estratégias específicas de enfrentamento, extraídas dos media sociais, nomeadamente, do Twitter. Esta abordagem multi-método permitiu uma análise qualitativa e quantitativa. Os resultados mostram uma forte associação entre a epidemia do Ébola e o continente Africano, com mais casos de contaminação humana identificados nos países africanos, apesar da percentagem real ser de zero casos. A análise qualitativa dos dados recolhidos no Twitter demonstrou menções diretas e indiretas ao grupo social – África / Africanos / países africanos – bem como identificação de outros grupos sociais – por exemplo, o governo, os meios de comunicação e outras entidades – que foram alvos de culpabilização não só pela epidemia em si, como também pela sua amplificação social. De um modo geral, estes resultados são relevantes para gestores e comunicadores de crises de saúde, tendo em conta que os efeitos do othering simbólico podem ser encontrados quando as pessoas percecionam eventos relacionados com saúde enquanto ameaças e que podem, eventualmente, resultar em processos de estigmatização social

A Survey on Visual Analytics of Social Media Data

The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..