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.

Social media has now become the de facto information source on real world events. The challenge, however, due to the high volume and velocity nature of social media streams, is in how to follow all posts pertaining to a given event over time -a task referred to as story detection. Moreover, there are often several different stories pertaining to a given event, which we refer to as sub-stories and the corresponding task of their automatic detectionas sub-story detection. This paper proposes hierarchical Dirichlet processes (HDP), a probabilistic topic model, as an effective method for automatic substory detection. HDP can learn sub-topics associated with sub-stories which enables it to handle subtle variations in sub-stories. It is compared with stateof-the-art story detection approaches based on locality sensitive hashing and spectral clustering. We demonstrate the superior performance of HDP for sub-story detection on real world Twitter data sets using various evaluation measures. The ability of HDP to learn sub-topics helps it to recall the substories with high precision. Another contribution of this paper is in demonstrating that the conversational structures within the Twitter stream can be used to improve sub-story detection performance significantly.

Abstract. Ordinal regression problem arises in situations where examples are rated in an ordinal scale. In practice, labeled ordinal data are difficult to obtain while unlabeled ordinal data are available in abundance. Designing a probabilistic semi-supervised classifier to perform ordinal regression is challenging. In this work, we propose a novel approach for semi-supervised ordinal regression using Gaussian Processes (GP). It uses the expectation-propagation approximation idea, widely used for GP ordinal regression problem. The proposed approach makes use of unlabeled data in addition to the labeled data to learn a model by matching ordinal label distributions approximately between labeled and unlabeled data. The resulting mixed integer programming problem, involving model parameters (real-valued) and ordinal labels (integers) as variables, is solved efficiently using a sequence of alternating optimization steps. Experimental results on synthetic, bench-mark and real-world data sets demonstrate that the proposed GP based approach makes effective use of the unlabeled data to give better generalization performance (on the absolute error metric, in particular) than the supervised approach. Thus, it is a useful approach for probabilistic semi-supervised ordinal regression problem.

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