Rob Procter

Rob Procter, Peter Tolmie, Mark Rouncefield

The need for AI systems to provide explanations for their behaviour is now widely recognised as key to their adoption. In this paper, we examine the problem of trustworthy AI and explore what delivering this means in practice, with a focus on healthcare applications. Work in this area typically treats trustworthy AI as a problem of Human-Computer Interaction involving the individual user and an AI system. However, we argue here that this overlooks the important part played by organisational accountability in how people reason about and trust AI in socio-technical settings. To illustrate the importance of organisational accountability, we present findings from ethnographic studies of breast cancer screening and cancer treatment planning in multidisciplinary team meetings to show how participants made themselves accountable both to each other and to the organisations of which they are members. We use these findings to enrich existing understandings of the requirements for trustworthy AI and to outline some candidate solutions to the problems of making AI accountable both to individual users and organisationally. We conclude by outlining the implications of this for future work on the development of trustworthy AI, including ways in which our proposed solutions may be re-used in different application settings.

M. Arana-Catania, Elena Kochkina, Arkaitz Zubiaga et al.

We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pandemic. We first describe the construction of the novel PANACEA dataset consisting of heterogeneous claims on COVID-19 and their respective information sources. The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims. We then propose novel techniques for automated veracity assessment based on Natural Language Inference including graph convolutional networks and attention based approaches. We have carried out experiments on evidence retrieval and veracity assessment on the dataset using the proposed techniques and found them competitive with SOTA methods, and provided a detailed discussion.

Runcong Zhao, Miguel Arana-Catania, Lixing Zhu et al.

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.

Iman Munire Bilal, Bo Wang, Adam Tsakalidis et al.

We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain. The dataset contains summaries of tweets spanning a 2-year period and covers more topics than any other public Twitter summarisation dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarising news articles following a template separating factual information (main story) from author opinions. Our method differs from previous work on generating gold-standard summaries from social media, which usually involves selecting representative posts and thus favours extractive summarisation models. To showcase the dataset's utility and challenges, we benchmark a range of abstractive and extractive state-of-the-art summarisation models and achieve good performance, with the former outperforming the latter. We also show that fine-tuning is necessary to improve performance and investigate the benefits of using different sample sizes.

Lixing Zhu, Zheng Fang, Gabriele Pergola et al.

Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data. Existing approaches have relied heavily on supervised training that requires abundant annotations and pre-defined aspect categories. Instead, with the aim of leveraging the large amount of unannotated data now available on vaccination, we propose a novel semi-supervised approach for vaccine attitude detection, called VADet. A variational autoencoding architecture based on language models is employed to learn from unlabelled data the topical information of the domain. Then, the model is fine-tuned with a few manually annotated examples of user attitudes. We validate the effectiveness of VADet on our annotated data and also on an existing vaccination corpus annotated with opinions on vaccines. Our results show that VADet is able to learn disentangled stance and aspect topics, and outperforms existing aspect-based sentiment analysis models on both stance detection and tweet clustering.

Matthew Maufe, James Ravenscroft, Rob Procter et al.

Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training downstream models on it, and an online interface to facilitate human annotation of this generated data. Using this interface, crowdworkers labelled 1117 synthetic QA pairs, which we then used to fine-tune downstream models and improve domain-specific QA performance by 8.75 F1.

Zheng Fang, Lama Alqazlan, Du Liu et al.

Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface that enables users compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling.

M. Arana-Catania, F. A. Van Lier, Rob Procter

Today's conflicts are becoming increasingly complex, fluid and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace-making, or the identification of key conflict issues and their interdependence. International peace efforts appear ill-equipped to successfully address these challenges. While technology is already being experimented with and used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study contributes to emerging research on the use of state-of-the-art machine learning technologies and techniques in conflict mediation processes. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning can effectively support mediating teams by providing them with tools for knowledge management, extraction and conflict analysis. Apart from illustrating the potential of machine learning tools in conflict mediation, the paper also emphasises the importance of interdisciplinary and participatory, co-creation methodology for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation.

Jiayu Song, Iman Munire Bilal, Adam Tsakalidis et al.

Opinion summarisation synthesises opinions expressed in a group of documents discussing the same topic to produce a single summary. Recent work has looked at opinion summarisation of clusters of social media posts. Such posts are noisy and have unpredictable structure, posing additional challenges for the construction of the summary distribution and the preservation of meaning compared to online reviews, which has been so far the focus of opinion summarisation. To address these challenges we present \textit{WassOS}, an unsupervised abstractive summarization model which makes use of the Wasserstein distance. A Variational Autoencoder is used to get the distribution of documents/posts, and the distributions are disentangled into separate semantic and syntactic spaces. The summary distribution is obtained using the Wasserstein barycenter of the semantic and syntactic distributions. A latent variable sampled from the summary distribution is fed into a GRU decoder with a transformer layer to produce the final summary. Our experiments on multiple datasets including Twitter clusters, Reddit threads, and reviews show that WassOS almost always outperforms the state-of-the-art on ROUGE metrics and consistently produces the best summaries with respect to meaning preservation according to human evaluations.

Mahmud Elahi Akhter, Federico Ruggeri, Iman Munire Bilal et al.

Structured claim decomposition is often proposed as a solution for verifying complex, multi-faceted claims, yet empirical results have been inconsistent. We argue that these inconsistencies stem from two overlooked bottlenecks: evidence alignment and sub-claim error profiles. To better understand these factors, we introduce a new dataset of real-world complex claims, featuring temporally bounded evidence and human-annotated sub-claim evidence spans. We evaluate decomposition under two evidence alignment setups: Sub-claim Aligned Evidence (SAE) and Repeated Claim-level Evidence (SRE). Our results reveal that decomposition brings significant performance improvement only when evidence is granular and strictly aligned. By contrast, standard setups that rely on repeated claim-level evidence (SRE) fail to improve and often degrade performance as shown across different datasets and domains (PHEMEPlus, MMM-Fact, COVID-Fact). Furthermore, we demonstrate that in the presence of noisy sub-claim labels, the nature of the error ends up determining downstream robustness. We find that conservative "abstention" significantly reduces error propagation compared to aggressive but incorrect predictions. These findings suggest that future claim decomposition frameworks must prioritize precise evidence synthesis and calibrate the label bias of sub-claim verification models.

Rob Procter, Mark Rouncefield

Exploring the socio-technical challenges confronting the adoption of AI in organisational settings is something that has so far been largely absent from the related literature. In particular, research into requirements for trustworthy AI typically overlooks how people deal with the problems of trust in the tools that they use as part of their everyday work practices. This article presents some findings from an ongoing ethnographic study of how current tools are used in air traffic control work and what it reveals about requirements for trustworthy AI in air traffic control and other safety-critical application domains.

Iman Munire Bilal, Zheng Fang, Miguel Arana-Catania et al.

As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language processing (NLP) tools. In particular, we focus on automating stages within the systematic reviewing process that are time-intensive and repetitive for human annotators and which lend themselves to immediate scalability through tools such as information retrieval and summarisation guided by expert advice. The article concludes with a summary of lessons learnt regarding the integrated approach towards systematic reviews and future directions for improvement, including explainability.

Zheng Fang, Miguel Arana-Catania, Felix-Anselm van Lier et al.

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previously been used to reliably 'screen' articles for review - that is, identify relevant articles based on reviewers' inclusion criteria. The application of ML techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We therefore set out to develop a series of tools that would assist in the profiling and analysis of 1,952 publications on the theme of 'outcomes-based contracting'. Tools were developed for the following tasks: assign publications into 'policy area' categories; identify and extract key information for evidence mapping, such as organisations, laws, and geographical information; connect the evidence base to an existing dataset on the same topic; and identify subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of ML techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While ML techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analysing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.

Wenjia Zhang, Lin Gui, Rob Procter et al.

To seek reliable information sources for news events, we introduce a novel task of expert recommendation, which aims to identify trustworthy sources based on their previously quoted statements. To achieve this, we built a novel dataset, called NewsQuote, consisting of 23,571 quote-speaker pairs sourced from a collection of news articles. We formulate the recommendation task as the retrieval of experts based on their likelihood of being associated with a given query. We also propose a multi-layer ranking framework employing Large Language Models to improve the recommendation performance. Our results show that employing an in-context learning based LLM ranker and a multi-layer ranking-based filter significantly improve both the predictive quality and behavioural quality of the recommender system.

Iman Munire Bilal, Preslav Nakov, Rob Procter et al.

The task of rumour verification in social media concerns assessing the veracity of a claim on the basis of conversation threads that result from it. While previous work has focused on predicting a veracity label, here we reformulate the task to generate model-centric free-text explanations of a rumour's veracity. The approach is model agnostic in that it generalises to any model. Here we propose a novel GNN-based rumour verification model. We follow a zero-shot approach by first applying post-hoc explainability methods to score the most important posts within a thread and then we use these posts to generate informative explanations using opinion-guided summarisation. To evaluate the informativeness of the explanatory summaries, we exploit the few-shot learning capabilities of a large language model (LLM). Our experiments show that LLMs can have similar agreement to humans in evaluating summaries. Importantly, we show explanatory abstractive summaries are more informative and better reflect the predicted rumour veracity than just using the highest ranking posts in the thread.

Zheng Fang, Yulan He, Rob Procter

Most existing topic models rely on bag-of-words (BOW) representation, which limits their ability to capture word order information and leads to challenges with out-of-vocabulary (OOV) words in new documents. Contextualized word embeddings, however, show superiority in word sense disambiguation and effectively address the OOV issue. In this work, we introduce a novel neural topic model called the Contextlized Word Topic Model (CWTM), which integrates contextualized word embeddings from BERT. The model is capable of learning the topic vector of a document without BOW information. In addition, it can also derive the topic vectors for individual words within a document based on their contextualized word embeddings. Experiments across various datasets show that CWTM generates more coherent and meaningful topics compared to existing topic models, while also accommodating unseen words in newly encountered documents.

Wenjia Zhang, Lin Gui, Rob Procter et al.

To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic. To achieve the aim, we describe the construction of a novel NewsQuote dataset consisting of 24,031 quote-speaker pairs that appeared on a COVID-19 news corpus. We demonstrate an automatic pipeline for speaker and quote extraction via a BERT-based Question Answering model. Then, we formulate expert recommendations as document retrieval task by retrieving relevant quotes first as an intermediate step for expert identification, and expert retrieval by directly retrieving sources based on the probability of a query conditional on a candidate expert. Experimental results on NewsQuote show that document retrieval is more effective in identifying relevant experts for a given news topic compared to expert retrieval

Lama Alqazlan, Rob Procter, Michael Castelle

The introduction of online marketplace platforms has led to the advent of new forms of flexible, on-demand (or 'gig') work. Yet, most prior research concerning the experience of gig workers examines delivery or crowdsourcing platforms, while the experience of the large numbers of workers who undertake educational labour in the form of tutoring gigs remains understudied. To address this, we use a computational grounded theory approach to analyse tutors' discussions on Reddit. This approach consists of three phases including data exploration, modelling and human-centred interpretation. We use both validation and human evaluation to increase the trustworthiness and reliability of the computational methods. This paper is a work in progress and reports on the first of the three phases of this approach.

Jonathan Davies, Miguel Arana-Catania, Rob Procter et al.

In recent years participatory budgeting (PB) in Scotland has grown from a handful of community-led processes to a movement supported by local and national government. This is epitomized by an agreement between the Scottish Government and the Convention of Scottish Local Authorities (COSLA) that at least 1% of local authority budgets will be subject to PB. This ongoing research paper explores the challenges that emerge from this 'scaling up' or 'mainstreaming' across the 32 local authorities that make up Scotland. The main objective is to evaluate local authority use of the digital platform Consul, which applies Natural Language Processing (NLP) to address these challenges. This project adopts a qualitative longitudinal design with interviews, observations of PB processes, and analysis of the digital platform data. Thematic analysis is employed to capture the major issues and themes which emerge. Longitudinal analysis then explores how these evolve over time. The potential for 32 live study sites provides a unique opportunity to explore discrete political and social contexts which materialize and allow for a deeper dive into the challenges and issues that may exist, something a wider cross-sectional study would miss. Initial results show that issues and challenges which come from scaling up may be tackled using NLP technology which, in a previous controlled use case-based evaluation, has shown to improve the effectiveness of citizen participation.

M. Arana-Catania, Rob Procter, Yulan He et al.

We present work on summarising deliberative processes for non-English languages. Unlike commonly studied datasets, such as news articles, this deliberation dataset reflects difficulties of combining multiple narratives, mostly of poor grammatical quality, in a single text. We report an extensive evaluation of a wide range of abstractive summarisation models in combination with an off-the-shelf machine translation model. Texts are translated into English, summarised, and translated back to the original language. We obtain promising results regarding the fluency, consistency and relevance of the summaries produced. Our approach is easy to implement for many languages for production purposes by simply changing the translation model.

Jonathan Davies, M. Arana-Catania, Rob Procter et al.

Participatory budgeting (PB) is already well established in Scotland in the form of community led grant-making yet has recently transformed from a grass-roots activity to a mainstream process or embedded 'policy instrument'. An integral part of this turn is the use of the Consul digital platform as the primary means of citizen participation. Using a mixed method approach, this ongoing research paper explores how each of the 32 local authorities that make up Scotland utilise the Consul platform to engage their citizens in the PB process and how they then make sense of citizens' contributions. In particular, we focus on whether natural language processing (NLP) tools can facilitate both citizen engagement, and the processes by which citizens' contributions are analysed and translated into policies.

M. Arana-Catania, F. A. Van Lier, Rob Procter

Today's conflicts are becoming increasingly complex, fluid and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the range of conflict parties and the evolution of their political positions, the distinction between relevant and less relevant actors in peace making, or the identification of key conflict issues and their interdependence. International peace efforts appear increasingly ill-equipped to successfully address these challenges. While technology is being increasingly used in a range of conflict related fields, such as conflict predicting or information gathering, less attention has been given to how technology can contribute to conflict mediation. This case study is the first to apply state-of-the-art machine learning technologies to data from an ongoing mediation process. Using dialogue transcripts from peace negotiations in Yemen, this study shows how machine-learning tools can effectively support international mediators by managing knowledge and offering additional conflict analysis tools to assess complex information. Apart from illustrating the potential of machine learning tools in conflict mediation, the paper also emphasises the importance of interdisciplinary and participatory research design for the development of context-sensitive and targeted tools and to ensure meaningful and responsible implementation.

Iman Munire Bilal, Bo Wang, Maria Liakata et al.

Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence. We provide annotation guidelines and human annotations of thematic coherence by journalist experts. We subsequently investigate the efficacy of different automated evaluation metrics for the task. We consider a range of metrics including surface level metrics, ones for topic model coherence and text generation metrics (TGMs). While surface level metrics perform well, outperforming topic coherence metrics, they are not as consistent as TGMs. TGMs are more reliable than all other metrics considered for capturing thematic coherence in microblog clusters due to being less sensitive to the effect of time windows.

Zheng Fang, Yulan He, Rob Procter

Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. It has been increasingly widely adopted as a tool in the social sciences, including political science, digital humanities and sociological research in general. One desirable property of topic models is to allow users to find topics describing a specific aspect of the corpus. A possible solution is to incorporate domain-specific knowledge into topic modeling, but this requires a specification from domain experts. We propose a novel query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics, thus avoiding tedious work from domain experts. Our proposed approach is particularly attractive when the user-specified query has a low occurrence in a text corpus, making it difficult for traditional topic models built on word cooccurrence patterns to identify relevant topics. Experimental results demonstrate the effectiveness of our model in comparison with both classical topic models and neural topic models.

M. Arana-Catania, F. A. Van Lier, Rob Procter et al.

The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations. In this article, we report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct citizen participation in democratic decision-making processes. The main objectives are to explore if the application of Natural Language Processing (NLP) and machine learning can improve citizens' experience of digital citizen participation platforms. Taking as a case study the "Decide Madrid" Consul platform, which enables citizens to post proposals for policies they would like to see adopted by the city council, we used NLP and machine learning to provide new ways to (a) suggest to citizens proposals they might wish to support; (b) group citizens by interests so that they can more easily interact with each other; (c) summarise comments posted in response to proposals; (d) assist citizens in aggregating and developing proposals. Evaluation of the results confirms that NLP and machine learning have a role to play in addressing some of the barriers users of platforms such as Consul currently experience.

Rob Procter, Mark Rouncefield, Peter Tolmie

We examine the problem of explainable AI (xAI) and explore what delivering xAI means in practice, particularly in contexts that involve formal or informal and ad-hoc collaboration where agency and accountability in decision-making are achieved and sustained interactionally. We use an example from an earlier study of collaborative decision-making in screening mammography and the difficulties users faced when trying to interpret the behavior of an AI tool to illustrate the challenges of delivering usable and effective xAI. We conclude by setting out a study programme for future research to help advance our understanding of xAI requirements for safe and ethical AI.

Jonathan Davies, Rob Procter

Trust and confidence in democratic institutions is at an all-time low. At the same time, many of the complex issues faced by city administrators and politicians remain unresolved. To tackle these concerns, many argue that citizens should, through the use of digital platforms, have greater involvement in decision-making processes. This paper describes research into two such platforms, 'Decide Madrid' and 'Better Reykjavik'. Through the use of interviews, questionnaires, ethnographic observation, and analysis of platform data, the study will determine if these platforms provide greater participation or simply replicate what is already offered by numerous other digital tools. The findings so far suggest that to be successful platforms must take on a form of deliberative democracy, allowing for knowledge co-production and the emergence of collective intelligence. Based on this, we aim to identify key features of sustainable models of online participation.

Arkaitz Zubiaga, Elena Kochkina, Maria Liakata et al.

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.

Leon Derczynski, Kalina Bontcheva, Maria Liakata et al.

Media is full of false claims. Even Oxford Dictionaries named "post-truth" as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the kind of discourse there is around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics - each having their own families of claims and replies - and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.

Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva et al.

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e. pieces of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how natural language processing and data mining techniques may be used to find ways of determining their veracity. In this survey we introduce and discuss two types of rumours that circulate on social media; long-standing rumours that circulate for long periods of time, and newly-emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far towards the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for detection and resolution of rumours.

Arkaitz Zubiaga, Bo Wang, Maria Liakata et al.

Social media and data mining are increasingly being used to analyse political and societal issues. Here we undertake the classification of social media users as supporting or opposing ongoing independence movements in their territories. Independence movements occur in territories whose citizens have conflicting national identities; users with opposing national identities will then support or oppose the sense of being part of an independent nation that differs from the officially recognised country. We describe a methodology that relies on users' self-reported location to build large-scale datasets for three territories -- Catalonia, the Basque Country and Scotland. An analysis of these datasets shows that homophily plays an important role in determining who people connect with, as users predominantly choose to follow and interact with others from the same national identity. We show that a classifier relying on users' follow networks can achieve accurate, language-independent classification performances ranging from 85% to 97% for the three territories.

Peter Tolmie, Rob Procter, David William Randall et al.

Social media and user-generated content (UGC) are increasingly important features of journalistic work in a number of different ways. However, their use presents major challenges, not least because information posted on social media is not always reliable and therefore its veracity needs to be checked before it can be considered as fit for use in the reporting of news. We report on the results of a series of in-depth ethnographic studies of journalist work practices undertaken as part of the requirements gathering for a prototype of a social media verification 'dashboard' and its subsequent evaluation. We conclude with some reflections upon the broader implications of our findings for the design of tools to support journalistic work.

Arkaitz Zubiaga, Maria Liakata, Rob Procter

Breaking news leads to situations of fast-paced reporting in social media, producing all kinds of updates related to news stories, albeit with the caveat that some of those early updates tend to be rumours, i.e., information with an unverified status at the time of posting. Flagging information that is unverified can be helpful to avoid the spread of information that may turn out to be false. Detection of rumours can also feed a rumour tracking system that ultimately determines their veracity. In this paper we introduce a novel approach to rumour detection that learns from the sequential dynamics of reporting during breaking news in social media to detect rumours in new stories. Using Twitter datasets collected during five breaking news stories, we experiment with Conditional Random Fields as a sequential classifier that leverages context learnt during an event for rumour detection, which we compare with the state-of-the-art rumour detection system as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying a piece of information to deem it a rumour, but instead we detect rumours from the tweet alone by exploiting context learnt during the event. Our classifier achieves competitive performance, beating the state-of-the-art classifier that relies on querying tweets with improved precision and recall, as well as outperforming our best baseline with nearly 40% improvement in terms of F1 score. The scale and diversity of our experiments reinforces the generalisability of our classifier.

Arkaitz Zubiaga, Elena Kochkina, Maria Liakata et al.

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.

Michal Lukasik, Kalina Bontcheva, Trevor Cohn et al.

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted.

Arkaitz Zubiaga, Alex Voss, Rob Procter et al.

In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets at the country level, which is so far unexplored in a real-time scenario. We analyse the extent to which a tweet's country of origin can be determined by making use of eight tweet-inherent features for classification. Furthermore, we use two datasets, collected a year apart from each other, to analyse the extent to which a model trained from historical tweets can still be leveraged for classification of new tweets. With classification experiments on all 217 countries in our datasets, as well as on the top 25 countries, we offer some insights into the best use of tweet-inherent features for an accurate country-level classification of tweets. We find that the use of a single feature, such as the use of tweet content alone -- the most widely used feature in previous work -- leaves much to be desired. Choosing an appropriate combination of both tweet content and metadata can actually lead to substantial improvements of between 20\% and 50\%. We observe that tweet content, the user's self-reported location and the user's real name, all of which are inherent in a tweet and available in a real-time scenario, are particularly useful to determine the country of origin. We also experiment on the applicability of a model trained on historical tweets to classify new tweets, finding that the choice of a particular combination of features whose utility does not fade over time can actually lead to comparable performance, avoiding the need to retrain. However, the difficulty of achieving accurate classification increases slightly for countries with multiple commonalities, especially for English and Spanish speaking countries.

Peter Tolmie, Rob Procter, Mark Rouncefield et al.

Inspired by a European project, PHEME, that requires the close analysis of Twitter-based conversations in order to look at the spread of rumors via social media, this paper has two objectives. The first of these is to take the analysis of microblogs back to first principles and lay out what microblog analysis should look like as a foundational programme of work. The other is to describe how this is of fundamental relevance to Human-Computer Interaction's interest in grasping the constitution of people's interactions with technology within the social order. Our critical finding is that, despite some surface similarities, Twitter-based conversations are a wholly distinct social phenomenon requiring an independent analysis that treats them as unique phenomena in their own right, rather than as another species of conversation that can be handled within the framework of existing Conversation Analysis. This motivates the argument that Microblog Analysis be established as a foundationally independent programme, examining the organizational characteristics of microblogging from the ground up. We articulate how aspects of this approach have already begun to shape our design activities within the PHEME project.

Arkaitz Zubiaga, Maria Liakata, Rob Procter et al.

The spread of false rumours during emergencies can jeopardise the well-being of citizens as they are monitoring the stream of news from social media to stay abreast of the latest updates. In this paper, we describe the methodology we have developed within the PHEME project for the collection and sampling of conversational threads, as well as the tool we have developed to facilitate the annotation of these threads so as to identify rumourous ones. We describe the annotation task conducted on threads collected during the 2014 Ferguson unrest and we present and analyse our findings. Our results show that we can collect effectively social media rumours and identify multiple rumours associated with a range of stories that would have been hard to identify by relying on existing techniques that need manual input of rumour-specific keywords.

Bo Wang, Arkaitz Zubiaga, Maria Liakata et al.

Social spam produces a great amount of noise on social media services such as Twitter, which reduces the signal-to-noise ratio that both end users and data mining applications observe. Existing techniques on social spam detection have focused primarily on the identification of spam accounts by using extensive historical and network-based data. In this paper we focus on the detection of spam tweets, which optimises the amount of data that needs to be gathered by relying only on tweet-inherent features. This enables the application of the spam detection system to a large set of tweets in a timely fashion, potentially applicable in a real-time or near real-time setting. Using two large hand-labelled datasets of tweets containing spam, we study the suitability of five classification algorithms and four different feature sets to the social spam detection task. Our results show that, by using the limited set of features readily available in a tweet, we can achieve encouraging results which are competitive when compared against existing spammer detection systems that make use of additional, costly user features. Our study is the first that attempts at generalising conclusions on the optimal classifiers and sets of features for social spam detection over different datasets.