Michal Lukasik

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Rumours on social media exhibit complex temporal patterns. This paper develops a model of rumour prevalence using a point process, namely a log-Gaussian Cox process , to infer an underlying continuous temporal probabilistic model of post frequencies. To generalize over different rumours , we present a multi-task learning method parametrized by the text in(More)
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(More)
The aim of this paper is to investigate suitable evaluation strategies for the task of word-level quality estimation of machine translation. We suggest various metrics to replace F 1-score for the " BAD " class, which is currently used as main metric. We compare the metrics' performance on real system outputs and synthetically generated datasets and suggest(More)
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(More)
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(More)
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(More)
Research on modeling time series text corpora has typically focused on predicting what text will come next, but less well studied is predicting when the next text event will occur. In this paper we address the latter case, framed as modeling continuous inter-arrival times under a log-Gaussian Cox process, a form of inhomo-geneous Poisson process which(More)