Sebastián A. Ríos

Topic-based social network analysis for virtual communities of interests in the Dark Web

The study of extremist groups and their interaction is a crucial task in order to maintain homeland security and peace. Tools such as social networks analysis and text mining have contributed to the understanding of this kind of groups in order to develop counter-terrorism applications. This work addresses the topic-based community key members extraction problem, for which our method combines both text mining and social network analysis techniques. This is achieved by first applying latent Dirichlet allocation to build two topic-based social networks: one social network oriented towards the thread creator point-of-view, and the other one oriented towards the repliers of the overall forum. Then, by using different Social Network Analysis measures, topic-based key members are evaluated using as benchmark a social network built using the plain documents. Experiments were performed using an English language based forum available in the Dark Web portal.

Towards the identification of keywords in the web site text content: A methodological approach

Since the creation of the web, the designers are looking for friendlier ways of make web page contents, which pictures, sounds, movies and free texts attract the users’ interest. Special attention receive the text content, because is the most frequently parameter used to retrieve information from the web. A simple way in order to understand the user’s text preferences, could be collect the words used in a searching. However, this information is only well‐know for the owner of the specific searching engine. In this paper we introduce a methodology in order to extract the most interest words for a user in a particular web site, based of the user browsing behavior and the web page text content. The methodology was tested using data originated in a bank web site showing the effectiveness of our approach.

Virtual Communities of Practice's Purpose Evolution Analysis Using a Concept-Based Mining Approach

Today, social networks systems have become more and more important. People have change their way to relate and communicate. Therefore, how to enhance contents and organization of a social network is a very important task. This way, we can help Virtual communities of practice (VCoP) to survive through time. VCoP are special kind of social network where the purpose is a key aspect. However, administrators are blind when trying to identify how to enhance the community. We propose a method which helps them by analyzing how purpose evolves through time. The approach has been experimentally tested in a real site with successful results.

Extending market basket analysis with graph mining techniques: A real case

A common problem for many companies, like retail stores, it is to find sets of products that are sold together. The only source of information available is the history of sales transactional data. Common techniques of market basket analysis fail when processing huge amounts of scattered data, finding meaningless relationships. We developed a novel approach for market basket analysis based on graph mining techniques, able to process millions of scattered transactions. We demonstrate the effectiveness of our approach in a wholesale supermarket chain and a retail supermarket chain, processing around 238,000,000 and 128,000,000 transactions respectively compared to classical approach.

Latent semantic analysis and keyword extraction for phishing classification

Phishing email fraud has been considered as one of the main cyber-threats over the last years. Its development has been closely related to social engineering techniques, where different fraud strategies are used to deceit a naïve email user. In this work, a latent semantic analysis and text mining methodology is proposed for the characterisation of such strategies, and further classification using supervised learning algorithms. Results obtained showed that the feature set obtained in this work is competitive against previous phishing feature extraction methodologies, achieving promising results over different benchmark machine learning classification techniques.

Using SOFM to improve web site text content

We introduce a new method to improve web site text content by identifying the most relevant free text in the web pages. In order to understand the variations in web page text, we collect pages during a period. The page text content is then transformed into a feature vector and is used as input of a clustering algorithm (SOFM), which groups the vectors by common text content. In each cluster, a centroid and its neighbor vectors are extracted. Then using a reverse clustering analysis, the pages represented by each vector are reviewed in order to find the similar. Furthermore, the proposed method was tested in a real web site, proving the effectiveness of this approach.

Dark Web portal overlapping community detection based on topic models

A hot research topic is the study and monitoring of online communities. Of course, homeland security institutions from many countries are using data mining techniques to perform this task, aiming to anticipate and avoid a possible menace to local peace. Tools such as social networks analysis and text mining have contributed to the understanding of these kinds of groups in order to develop counter-terrorism applications. A key application is the discovery of sub-communities of interests which main topic could be a possible homeland security threat. However, most algorithms detect disjoint communities, which means that every community member belongs to a single community. Thus, final conclusions can be omitting valuable information which leads to wrong results interpretations. In this paper, we propose a novel approach to combine traditional network analysis methods for overlapping community detection with topic-model based text mining techniques. Afterwards, we developed a sub-community detection algorithm that allow each member belong more than one sub-community. Experiments were performed using an English language based forum available in the Dark Web portal (islamicAwakening).

Topic-based social network analysis for virtual communities of interests in the dark web

The study of extremist groups and their interaction is a crucial task in order to maintain homeland security and peace. Tools such as social networks analysis and text mining have contributed to their understanding in order to develop counter-terrorism applications. This work addresses the topic-based community key-members extraction problem, for which our method combines both text mining and social network analysis techniques. This is achieved by first applying latent Dirichlet allocation to build two topic-based social networks in online forums: one social network oriented towards the thread creator point-of-view, and the other is oriented towards the repliers of the overall forum. Then, by using different network analysis measures, topic-based key members are evaluated using as benchmark a social network built a plain representation of the network of posts. Experiments were successfully performed using an English language based forum available in the Dark Web portal.

Hypergeometric language model and zipf-like scoring function for web document similarity retrieval

The retrieval of similar documents in the Web from a given document is different in many aspects from information retrieval based on queries generated by regular search engine users. In this work, a new method is proposed for Web similarity document retrieval based on generative language models and meta search engines. Probabilistic language models are used as a random query generator for the given document. Queries are submitted to a customizable set of Web search engines. Once all results obtained are gathered, its evaluation is determined by a proposed scoring function based on the Zipf law. Results obtained showed that the proposed methodology for query generation and scoring procedure solves the problem with acceptable levels of precision.

Conceptual classification to improve a web site content

This paper presents a conceptual based approach for improving a Web site content. Usually Web Usage Mining (WUM) techniques study the visitors’ browsing behavior to obtain interesting knowledge. However, most of the work in the area leave behind the semantic information of web pages. We propose to combine the Concept-Based Knowledge Discovery in Text with the visitors sessions to perform the personalization task. This way, it is possible to obtain information about which are the users’ goals when browsing a web site. Moreover, it is possible to give better browsing recomendations and help managers improving the content of their Web site. We test this idea on a real Web site to show its effectiveness.