Elena Kochkina


2020

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Estimating predictive uncertainty for rumour verification models
Elena Kochkina | Maria Liakata
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. We show that these estimates can be used to filter out model predictions likely to be erroneous so that these difficult instances can be prioritised by a human fact-checker. We propose two methods for uncertainty-based instance rejection, supervised and unsupervised. We also show how uncertainty estimates can be used to interpret model performance as a rumour unfolds.

2019

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Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data
Harish Tayyar Madabushi | Elena Kochkina | Michael Castelle
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda

The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprising. However, propaganda detection, like other tasks that deal with news documents and other forms of decontextualized social communication (e.g. sentiment analysis), inherently deals with data whose categories are simultaneously imbalanced and dissimilar. We show that BERT, while capable of handling imbalanced classes with no additional data augmentation, does not generalise well when the training and test data are sufficiently dissimilar (as is often the case with news sources, whose topics evolve over time). We show how to address this problem by providing a statistical measure of similarity between datasets and a method of incorporating cost-weighting into BERT when the training and test sets are dissimilar. We test these methods on the Propaganda Techniques Corpus (PTC) and achieve the second highest score on sentence-level propaganda classification.

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SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
Genevieve Gorrell | Elena Kochkina | Maria Liakata | Ahmet Aker | Arkaitz Zubiaga | Kalina Bontcheva | Leon Derczynski
Proceedings of the 13th International Workshop on Semantic Evaluation

Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of “fake news” has become a mainstream concern. However automated support for rumour verification remains in its infancy. It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour’s veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70% increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved.

2018

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All-in-one: Multi-task Learning for Rumour Verification
Elena Kochkina | Maria Liakata | Arkaitz Zubiaga
Proceedings of the 27th International Conference on Computational Linguistics

Automatic resolution of rumours is a challenging task that can be broken down into smaller components that make up a pipeline, including rumour detection, rumour tracking and stance classification, leading to the final outcome of determining the veracity of a rumour. In previous work, these steps in the process of rumour verification have been developed as separate components where the output of one feeds into the next. We propose a multi-task learning approach that allows joint training of the main and auxiliary tasks, improving the performance of rumour verification. We examine the connection between the dataset properties and the outcomes of the multi-task learning models used.

2017

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Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
Elena Kochkina | Maria Liakata | Isabelle Augenstein
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes team Turing’s submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour stance classification, which involves identifying the attitude of Twitter users towards the truthfulness of the rumour they are discussing. Stance classification is considered to be an important step towards rumour verification, therefore performing well in this task is expected to be useful in debunking false rumours. In this work we classify a set of Twitter posts discussing rumours into either supporting, denying, questioning or commenting on the underlying rumours. We propose a LSTM-based sequential model that, through modelling the conversational structure of tweets, which achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A.

2016

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Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Arkaitz Zubiaga | Elena Kochkina | Maria Liakata | Rob Procter | Michal Lukasik
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

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.