## 6 Topic Detection Based on Probabilistic Topic Models  
Exploring and retrieving meaningful information from large collections of textual documents is a challenging task. The hidden thematic structure in such collections can be discovered by applying recently proposed statistical analysis tools, such as probabilistic topic models [27]. At a high level, the idea is to study the co-occurrence of words, assuming that words that tend to co-occur frequently belong to or express the same semantic concept. The purpose of the topic detection module is to uncover the hidden thematic structure of a document collection related to an ARCOMEM campaign and to identify semantic topics of interest for future data analysis and navigation. The detected topics provide a low-dimensional representation, in terms of the abstract co-occurrence patterns, of the textual data analyzed. After detecting topics, the projection of each document into the topic space can be exploited to classify documents and named entities into semantic categories, allowing a more effective browsing of the content crawled during each campaign. The simple instantiation of probabilistic topic model is known as latent Dirichlet allocation [28]. Its underlying generative process can be formalized as follows:  
1. For each latent topic \(k=1,\ldots,K\) sample a multinomial distribution over words \(\boldsymbol{\phi}_{k}\sim\textit{Dir}(\boldsymbol{\beta})\);
2. For each document \(d\) in the corpus, 1. Sample the number \(n_{d}\) of words to be generated; 2. Choose \(\boldsymbol{\theta}_{d}\sim\textit{Dir}(\boldsymbol{\alpha})\); 3. For each of the \(n_{d}\) words to be generated, 1. Sample a topic \(z\sim\textit{Discrete}(\boldsymbol{\theta}_{d})\) 2. Sample \(w\sim\textit{Discrete}(\boldsymbol{\phi}_{z})\)  
Exact inference in Latent Dirichlet Allocation (LDA) is intractable, due to the exponential number of possible word-factor assignments. The solution to this is to use approximate inference algorithms, such as mean-field variational approximation [28], expectation propagation [29] and Gibbs sampling [30]. The topic detection module in ARCOMEM is based on the open source implementation of a collapsed variational Bayes inference procedure provided by [PERSON] [31]. The nature of the documents crawled within a campaign could be intrinsically multilingual, since we are often interested in tracking events with importance at the worldwide dimension. As a result, running the topic module on the whole textual collection corresponding to a crawl could produce low quality results: (i) the low-dimensional decomposition of documents could simply try to identify language-specific topics (English, German, _etc._); (ii) from the user point of view, topics should be directly interpretable by analyzing their keywords, and the language of users should hence be considered. A preliminary evaluation by users on the topics detected on the U.S. Elections 2012 data highlighted the importance of addressing these issues. In particular, users were reported to engage with topics, by using them as a tool for browsing the collection of documents, but also to have problems in understanding some of the keywords associated with a topic, since they belonged to a different language. This valuable feedback motivated the integration of a language selection phase into the data pre-processing module. An updated version of the topic detection module takes as input a list of languages that should be considered for the analysis. Language detection is accomplished by integrating and running a detector based on a naive Bayes open source implementation [32]. To enhance the capabilities of topics as a tool for organizing and browsing a collection of large textual data, it is interesting to detect relationships between topics. The connection between topics and relevant documents allows users to navigate documents, focus on the ones they are interested in and keep exploring data by browsing documents having the same topics.