A Buzz and E-Reputation Monitoring Tool for Twitter Based on Galois Lattices

Abstract

Abstract. In the actual interconnected world, the speed of broadcasting of information leads the formation of opinions towards more and more immediacy. Big social networks, by allowing distribution, and therefore broadcasting of information in a almost instantaneous way, also speed up the formation of opinions concerning actuality. Then, these networks are great observatories of opinions and e-reputation. In this e-reputation monitoring task, it is easy to get a set of information (web pages, blog pages, tweets,...) containing a chosen word or a set of words ( a company name, a domain of interest,...), and then we can easily search for the most used words. But a harder, but more interesting task, is to track the set of jointly used words in this dataset, because this latter contains the more shared advice about the initial searched set of words. Precisely, the exhaustive discovering of the shared properties of a collection of objects is the main task of the Galois lattices used in the Formal Concept Analysis. In this article we state clearly the characteristics, advantages and constraints of one of the more successful online social networks: Twitter. Then we detail the difficult task of tracking, on Twitter, the most forwarded information about a chosen subject. We also explain how the characteristics of Galois lattices permit to solve elegantly and efficiently this problem. But, retrieving the most used corpus of words is not enough, we have to show the results in an informative and readable manner, which is not easy when the result is a Galois Lattice. Then we propose a visualisation called topigraphic network of tags, which represent a tag cloud in a network of concepts with a topographic allegory, which permits to visualise the more important concepts found about a given search on Twitter.

Using implications from FCA to represent a two mode network data

“…Cuvelier and Aufaure [16], analyzed tweets about a specific subject. By means of FCA, the authors were able to establish relationships between messages and, after representing them in the lattice, use data filtering criteria to assemble a topographical network graph to help clarify the information obtained.…”

Knowledge discovery in social networks by using a logic-based treatment of implications

“…Thus, in [28] an ontology-based technique is proposed for a more fine-grained sentiment analysis of Twitter posts. Concerning social networks, FCA has been considered a great tool to study social communities [12]; in [5] the authors consider objects as members and their attributes as their contacts and build the formal context on which the concept lattice is generated. This lattice is then used to calculate statistics, which the authors call Conceptual Relatedness and Closeness, about every member of the social network.…”

Minimal implications base for social network analysis

“…Freeman (2005) and Rome and Haralick (2005) explore web communities. Cuvelier and Aufaure (2011) also carry out a study unifying the FCA and complex network theories. A socially-oriented work is presented by Poelmans et al (2011), where a semi-automatic process to expose a network of criminal organizations and their members is proposed.…”