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

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

QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs

This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B. While our submission for Task A did not exceed the baseline, retraining our model using all the training tweets, showed promising results leading to (f-avg 0.601) using bidirectional LSTM with BERT multilingual embedding for Task A. For our submission for Task B, we ranked 6th (f-avg 0.709). With further investigation, our best experimented settings increased performance from (f-avg 0.573) to (f-avg 0.733) with same architecture and parameter settings and after only incorporating social interaction features -- highlighting the impact of social interaction on the model's performance

Rumor Detection on Social Media: Datasets, Methods and Opportunities

Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fake news. Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques. This paper gives an overview of the recent studies in the rumor detection field. It provides a comprehensive list of datasets used for rumor detection, and reviews the important studies based on what types of information they exploit and the approaches they take. And more importantly, we also present several new directions for future research.Comment: 10 page

A knowledge regularized hierarchical approach for emotion cause analysis

Emotion cause analysis, which aims to identify the reasons behind emotions, is a key topic in sentiment analysis. A variety of neural network models have been proposed recently, however, these previous models mostly focus on the learning architecture with local textual information, ignoring the discourse and prior knowledge, which play crucial roles in human text comprehension. In this paper, we propose a new method to extract emotion cause with a hierarchical neural model and knowledge-based regularizations, which aims to incorporate discourse context information and restrain the parameters by sentiment lexicon and common knowledge. The experimental results demonstrate that our proposed method achieves the state-of-the-art performance on two public datasets in different languages (Chinese and English), outperforming a number of competitive baselines by at least 2.08% in F-measure

MultiFC:A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims

We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We present an in-depth analysis of the dataset, highlighting characteristics and challenges. Further, we present results for automatic veracity prediction, both with established baselines and with a novel method for joint ranking of evidence pages and predicting veracity that outperforms all baselines. Significant performance increases are achieved by encoding evidence, and by modelling metadata. Our best-performing model achieves a Macro F1 of 49.2%, showing that this is a challenging testbed for claim veracity prediction.Comment: Proceedings of EMNLP 2019, to appea

ghostwriter19 @ SardiStance: Generating new tweets to classify SardiStance EVALITA 2020 political tweets

Understanding the events and the dominant thought is of great help to convey the desired message to our potential audience, be it marketing or political propaganda.Succeeding while the event is still ongoing is of vital importance to prepare alerts that require immediate action.A micro message platform like Twitter is the ideal place to be able to read a large amount of data linked to a theme and self-categorized by its users using hashtags and mentions.In this research, I will show how a simple translator can be used to bring styles, vocabulary, grammar, and other characteristics to a common factor that leads each of us to be unique in the way we express ourselves.Comprendere gli eventi e il pensiero dominante è di grande aiuto per veicolare alla nostra potenziale audience il messaggio desiderato sia esso di marketing o di propaganda politica.Riuscirci mentre l'evento è ancora in corso è di vitale importanza per predisporre alert che richiedono un intervento immediato.Una piattaforma di micro messaggi come Twitter è il luogo ideale per poter leggere una grande quantità di dati legata ad un tema, e spesso auto categorizzati dai suoi stessi utenti per mezzo di hashtag e menzioni.In questa ricerca mostrerò come un semplice traduttore può essere usato per portare a fattor comune stili, lessico, grammatica e altre caratteristiche che portano ognuno di noi ad essere unico nel modo di esprimersi