MOVES

People tweet more than 500 Million times daily, yielding a noisy, informal, but sometimes informative corpus of 140-character messages that mirrors the zeitgeist in an unprecedented manner. In the domain of surveillance systems, one is interested in extracting the specific information about "who" is doing "what", "where" and "when", that is conveyed in these short messages. This task is commonly called named entity recognition and aims to automatically extract mentions of rigid designators from text belonging to named-entity types such as persons, organizations, events, locations and timexes.
In this project, we propose to improve the robustness of named entity recognition systems for Twitter starting by following a line like the recent work of (Saha et al., 2015a) with biomedical texts. Indeed, most existing systems learn classifiers based on small amount of labeled data. Although strong results can be obtained through cross-validation, these systems may not scale up in real-world environments such as social media due to high variety of unseen text contents. One solution to this problem is to apply ensemble learning techniques to take advantages of different learning paradigms such as conditional random fields or support vector machines (Joachims, 1998) that may combine in some optimum consensus. By doing so, we expect that robust classifiers can be built and used reliably in a real-word environment such as Twitter.
To bridge the gap between unstructured text and structured machine readable knowledge bases, entity linking is performed and consists in mapping each entity mention in a tweet to a unique entity, i.e. an entry ID of a knowledge base such as Wikipedia or YAGO (Suchanek et al., 2007). As such, each tweet is not an isolated segment of text but instead links to a knowledge base, which allows multilingual reasoning. A great deal of studies has been tackling entity linking (Ferragina and Scaiella, 2013) and more recently entity linking for social media texts (Liu et al., 2013). Tweets pose special challenges to entity linking. First, a tweet is often too concise and too noisy to provide enough information for similarity computing. Second, tweets have rich variations of named entities, and many of them fall out of the scope of the knowledge bases. Within the scope of this project, we propose to tune the strategy used for robust entity linking in (Hoffart et al., 2011) for social media texts. We will study the introduction of named entity continuous space (Lin et al., 2015) in the disambiguation process. A promising new possibility can be the introduction of the recent work of one of the team members (Brazdil et al., 2015) in the domain of affinity mining, specially to discover and resolve apparently unrelated entities, mentioned in the social networks.

Each cluster of tweet messages focusing on a bursty topic may constitute a potential threat. However, the overwhelming majority of clusters are armless and represent casual, conventional or expressive crowds as well as noisy data (Becker et al., 2011). To identify acting or protest crowds, we propose to understand the typical language usage present in each cluster as well as its network activity. Indeed, ultimately, a crowd is characterized by its dominant emotion, its level of interaction and shared focus.
(Krumm, 2015) showed that specific radicalized language is used within acting and protest crowds. Therefore, we propose that each tweet inside a cluster is classified as radical or non-radical in terms of language use, so that the collective radicalization of a cluster can be measured. As far as we know, there exists no previous work on modeling radicalized language. Radicalization is a process by which an individual or group comes to adopt increasingly extreme political, social, or religious ideals and aspirations (goo.gl/Jz08cD). As such, we hypothesize that radicalized language mainly expresses negative emotions (such as anger, fear, or anxiety) with high intensity, following the classification of Plutchik's wheel of emotion (Plutchik, 1980).
In this project we propose to learn weak classifiers based on dictionaries of emotional words (Qadir and Riloff, 2014) to retrieve many roughly classified emotional tweets, in a similar way as (Tang et al., 2014). We will build word embeddings that consider emotional and intensity contexts at the same time together with syntactic context. For that purpose, weak classifiers for intensity detection will be built based on recent work of (Sharma et al., 2015) on language intensity.
Finally, we will study the introduction of demographically-driven word embeddings. Recently, (Bamman et al., 2014) proposed to develop word embeddings considering the localization of the issuer of the conveyed message. These findings open a great deal of improvements, as tweets can be geo-localized but also include information such as age and gender of the issuer.
Each event cluster is supposed to focus on a specific event (i.e. high focus), that may imply radicalized talks (high emotion) of its members. One particularity of crowds is also its level of internal relationship. A community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group. This problem has intensively been investigated in recent years (Pizzuti, 2008). In this part of the project, we propose to develop a new algorithm capable of optimizing different parallel fitness functions to identify densely connected groups of nodes with sparse connections between groups following the multi-objective paradigm (Coello, 1999).