Web Mining and Social Networking: Techniques and ... - tud.ttu.ee

7.4 Socio-Sense: A System for Analyzing the Societal Behavior from Web Archive 1617.3.4 Community Discovery ExamplesIn [164], an interesting result given is based on a NEC blog dataset. The blog data was collectedby an NEC in-house crawler. The crawled blog dataset contains 148,681 entry-to-entrylinks between 407 blogs over 12 consecutive month (one month corresponds to one time stampin the networked dataset) between August 2005 and September 2006. To determine the numberof communities, Soft Modularity metric [191] is introduced to measure the goodness of thecommunities derived. The larger the soft modularity value the better result of community partitionis obtained. Thus first the blog dataset is transformed into a single networked graph andits soft modularity values Q s are calculated and plotted in Fig.7.13(a). As seen from the figure,a clear peak of Q s is located at the community number being 4. Then, based on this optimizedcommunity number, four aggregated community structures were extracted by using the graphpartition algorithm [164], which are shown in Fig.7.13. In addition to the aggregated commu-Fig. 7.13. (a) Soft modularity and (b) mutual information under different α for the NEC dataset[164]nity structures, in Fig.7.14 the top keywords measured by the tf/idf scores that contained infour corresponding communities are listed as well. Furthermore, by interpreting the top keywords(i.e. the representatives of communities), we can see the essential properties of variouscommunities. For example, C1 is about technology, C2 about politics, C3 about entertainment,and C4 about digital libraries [164]. With the derived community structures as ground truthof communities, the proposed algorithm is employed to determine the community membershipsby taking the evolutions of community into consideration. By comparing the differenceof the ground truth and dynamically extracted results, the mutual information between themis plotted in Fig.7.13(b) under different α values. The plots indicate that when α increases,emphasizing less on the temporal smoothness, the extracted community structures are muchdeviated from the ground truth at each time stamp and have high variation over time. As aresult, the concluded results reveal the dynamic evolutions of communities in-depth, whichagain justifies the strong capability of social network analysis in dynamic environments. Moreanalysis insights are seen in [164].7.4 Socio-Sense: A System for Analyzing the Societal Behaviorfrom Web ArchiveM. Kitsuregawa et al. [136] introduce a Socio-Sense Web analysis system. The system appliesstructural and temporal analysis methods to long term Web archive to obtain insight into the