Classifying Tweet Level Judgements of Rumours in Social Media

Social media is a rich source of rumours and corresponding community reactions. Rumours reflect different characteristics, some shared and some individual. We formulate the problem of classifying tweet level judgements of rumours as a supervised learning task. Both supervised and unsupervised domain adaptation are considered, in which tweets from a rumour are classified on the basis of other annotated rumours. We demonstrate how multi-task learning helps achieve good results on rumours from the 2011 England riots

Probabilistic Modeling of Rumour Stance and Popularity in Social Media

Social media tends to be rife with rumours when new reports are released piecemeal during breaking news events. One can mine multiple reactions expressed by social media users in those situations, exploring users’ stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. Moreover, rumours in social media exhibit complex temporal patterns. Some rumours are discussed with an increasing number of tweets per unit of time whereas other rumours fail to gain ground. This thesis develops probabilistic models of rumours in social media driven by two applications: rumour stance classification and modeling temporal dynamics of rumours. Rumour stance classification is the task of classifying the stance expressed in an individual tweet towards a rumour. Modeling temporal dynamics of rumours is an application where rumour prevalence is modeled over time. Both applications provide insights into how a rumour attracts attention from the social media community. These can assist journalists with their work on rumour tracking and debunking, and can be used in downstream applications such as systems for rumour veracity classification. In this thesis, we develop models based on probabilistic approaches. We motivate Gaussian processes and point processes as appropriate tools and show how features not considered in previous work can be included. We show that for both applications, transfer learning approaches are successful, supporting the hypothesis that there is a common underlying signal across different rumours. We furthermore introduce novel machine learning techniques which have the potential to be used in other applications: convolution kernels for streams of text over continuous time and a sequence classification algorithm based on point processes

It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models

Classical wisdom in machine learning holds that the generalization error can be decomposed into bias and variance, and these two terms exhibit a \emph{trade-off}. However, in this paper, we show that for an ensemble of deep learning based classification models, bias and variance are \emph{aligned} at a sample level, where squared bias is approximately \emph{equal} to variance for correctly classified sample points. We present empirical evidence confirming this phenomenon in a variety of deep learning models and datasets. Moreover, we study this phenomenon from two theoretical perspectives: calibration and neural collapse. We first show theoretically that under the assumption that the models are well calibrated, we can observe the bias-variance alignment. Second, starting from the picture provided by the neural collapse theory, we show an approximate correlation between bias and variance

Stance classification in rumours as a sequential task exploiting the tree structure of social media conversations

Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users’ replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations

Hawkes processes for continuous time sequence classification : an application to rumour stance classification in Twitter

Classification of temporal textual data sequences is a common task in various domains such as social media and the Web. In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information. Our experiments on rumour stance classification on four Twitter datasets show the importance of using the temporal information of tweets along with the textual content

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