Truth Discovery from MultiSourced Text Data Based on Ant Colony Optimization In the era of information explosion, plenty of data has been generated through a variety of channels, such as social networks, crowdsourcing platforms and blogs. Conflicts and errors are constantly emerging. Truth discovery aims to find trustworthy information from conflicting data by considering source reliability. However, most traditional truth discovery approaches are designed only for structured data, and fail to meet the strong requirements to extract trustworthy information from unstructured raw text data. The major challenges of inferring reliable information on text data stem from the multifactorial property (i.e., an answer may contain multiple different key factors, which may be complex) and the diversity of word usages (i.e., different words may share similar semantic information, but the spelling of which are completely different). To solve these challenges, an ant colony optimization based text data truth discovery model is proposed. Firstly, keywords extracted from the whole answers of the specific question are grouped into a set. Then, we translate the truth discovery problem to a subset optimization problem, and the parallel ant colony optimization is utilized to find correct keywords for each question based on the hypothesis of truth discovery from the whole keywords. After that, the answers to each question can be ranked based on the similarities between keywords of user answers and identified correct keywords found by colony. The experiment results on real dataset show that even the semantic information of text data is complex, our proposed model can still find trustworthy information from complex answers compared with retrievalbased and stateoftheart approaches.

A Meaningful Information Extraction System for Interactive Analysis of Documents This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers. The study presented in this paper tackles the particular problem of clustering topics at multilevels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multidimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the a priori number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best K for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a nontraditional visualization approach based on a multiagents system which combines both dimension reduction and interactivity.