On the Impact of Entity Linking in Microblog Real-Time Filtering

Microblogging is a model of content sharing in which the temporal locality of posts with respect to important events, either of foreseeable or unforeseeable nature, makes applica- tions of real-time filtering of great practical interest. We propose the use of Entity Linking (EL) in order to improve the retrieval effectiveness, by enriching the representation of microblog posts and filtering queries. EL is the process of recognizing in an unstructured text the mention of relevant entities described in a knowledge base. EL of short pieces of text is a difficult task, but it is also a scenario in which the information EL adds to the text can have a substantial impact on the retrieval process. We implement a start-of-the-art filtering method, based on the best systems from the TREC Microblog track realtime adhoc retrieval and filtering tasks , and extend it with a Wikipedia-based EL method. Results show that the use of EL significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 - 17, 201

Prototype/topic based Clustering Method for Weblogs

[EN] In the last 10 years, the information generated on weblog sites has increased exponentially, resulting in a clear need for intelligent approaches to analyse and organise this massive amount of information. In this work, we present a methodology to cluster weblog posts according to the topics discussed therein, which we derive by text analysis. We have called the methodology Prototype/Topic Based Clustering, an approach which is based on a generative probabilistic model in conjunction with a Self-Term Expansion methodology. The usage of the Self-Term Expansion methodology is to improve the representation of the data and the generative probabilistic model is employed to identify relevant topics discussed in the weblogs. We have modified the generative probabilistic model in order to exploit predefined initialisations of the model and have performed our experiments in narrow and wide domain subsets. The results of our approach have demonstrated a considerable improvement over the pre-defined baseline and alternative state of the art approaches, achieving an improvement of up to 20% in many cases. The experiments were performed on both narrow and wide domain datasets, with the latter showing better improvement. However in both cases, our results outperformed the baseline and state of the art algorithms.The work of the third author was carried out in the framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie, the DIANA APPLICATIONS Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Perez-Tellez, F.; Cardiff, J.; Rosso, P.; Pinto Avendaño, DE. (2016). Prototype/topic based Clustering Method for Weblogs. Intelligent Data Analysis. 20(1):47-65. https://doi.org/10.3233/IDA-150793S476520

Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

Follow-back Recommendations for Sports Bettors: A Twitter-based Approach

Social network based recommender systems are powered by a complex web of social discussions and user connections. Short text microblogs e.g. Twitter present powerful frameworks for information consumption, due to their real-time nature in content throughput as well as user connections. Therefore, users on such platforms consume the disseminated content to a greater or lesser extent based on their interests. Quantifying this degree of interest is a difficult task based on the amount of information that such platforms generate at any given time. Thus, the generation of personalized profiles based on the Degree of Interest (DoI) that users have towards certain topics in such short texts presents a research problem. We address this challenge by following a two-step process in generation of personalized sports betting related user profiles in tweets as a case study. We (i) compute the Degree of Interest in Sports Betting (DoiSB) of tweeters and (ii) affirm this DoiSB by correlating it with their friendship network. This is an integral process in the design of a short text based recommender systems for users to follow i.e follow-back recommendations as well as content-based recommendations relying on the interests of users on such platforms. In this paper, we described the DoiSB computation and follow-back recommendation process by building a vector representation model for tweets. We then use this model to profile users interested in sports betting. Experiments using real Twitter dataset geolocated to Kenya shows the effectiveness of our approach in the identification of tweeter\u27s DoiSBs as well as their correlation with their friendship network

Weblog and short text feature extraction and impact on categorisation

The characterisation and categorisation of weblogs and other short texts has become an important research theme in the areas of topic/trend detection, and pattern recognition, amongst others. The value of analysing and characterising short text is to understand and identify the features that can identify and distinguish them, thereby improving input to the classification process. In this research work, we analyse a large number of text features and establish which combinations are useful to discriminate between the different genres of short text. Having identified the most promising features, we then confirm our findings by performing the categorisation task using three approaches: the Gaussian and SVM classifiers and the K-means clustering algorithm. Several hundred combinations of features were analysed in order to identify the best combinations and the results confirmed the observations made. The novel aspect of our work is the detection of the best combination of individual metrics which are identified as potential features to be used for the categorisation process.The research work of the third author is partially funded by the WIQ-EI (IRSES grant n. 269180) and DIANA APPLICATIONS (TIN2012-38603-C02-01), and done in the framework of the VLC/Campus Microcluster on Multimodal Interaction in Intelligent Systems.Perez Tellez, F.; Cardiff, J.; Rosso, P.; Pinto Avendaño, DE. (2014). Weblog and short text feature extraction and impact on categorisation. Journal of Intelligent and Fuzzy Systems. 27(5):2529-2544. https://doi.org/10.3233/IFS-141227S2529254427

The role of geographic knowledge in sub-city level geolocation algorithms

Geolocation of microblog messages has been largely investigated in the lit- erature. Many solutions have been proposed that achieve good results at the city-level. Existing approaches are mainly data-driven (i.e., they rely on a training phase). However, the development of algorithms for geolocation at sub-city level is still an open problem also due to the absence of good training datasets. In this thesis, we investigate the role that external geographic know- ledge can play in geolocation approaches. We show how di)erent geographical data sources can be combined with a semantic layer to achieve reasonably accurate sub-city level geolocation. Moreover, we propose a knowledge-based method, called Sherloc, to accurately geolocate messages at sub-city level, by exploiting the presence in the message of toponyms possibly referring to the speci*c places in the target geographical area. Sherloc exploits the semantics associated with toponyms contained in gazetteers and embeds them into a metric space that captures the semantic distance among them. This allows toponyms to be represented as points and indexed by a spatial access method, allowing us to identify the semantically closest terms to a microblog message, that also form a cluster with respect to their spatial locations. In contrast to state-of-the-art methods, Sherloc requires no prior training, it is not limited to geolocating on a *xed spatial grid and it experimentally demonstrated its ability to infer the location at sub-city level with higher accuracy

Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis

This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitter platform’s user communities. Each community is trained with the embedded deep learning model. To recommend hashtags for the new user in the social network, the correlation between the tweets of such user and the knowledge graph of each community is explored to set the relevant communities of such user. The models of the relevant communities are used to infer the hashtags of the tweets of such users. We conducted extensive testing to demonstrate the usefulness of our methods on a variety of tweet collections. Experimental results show that the proposed approach is more efficient than the baseline approaches in terms of both runtime and accuracy.acceptedVersio