Conference Paper

Web Science 2.0: Identifying Trends through Semantic Social Network Analysis

DOI: 10.2139/ssrn.1299869 Conference: IEEE Conference on Social Computing (SocialCom-09)
Source: DBLP

ABSTRACT We introduce a novel set of social network analysis based algorithms for mining the Web, blogs, and online forums to identify trends and find the people launching these new trends. These algorithms have been implemented in Condor, a software system for predictive search and analysis of the Web and especially social networks. Algorithms include the temporal computation of network centrality measures, the visualization of social networks as Cybermaps, a semantic process of mining and analyzing large mounts of text based on social network analysis, and sentiment analysis and information filtering methods. The temporal calculation of betweenness of concepts permits to extract and predict long-term trends on the popularity of relevant concepts such as brands, movies, and politicians. We illustrate our approach by qualitatively comparing Web buzz and our Web betweenness for the 2008 US presidential elections, as well as correlating the Web buzz index with share prices.

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    ABSTRACT: This paper considers the extraction and analysis of Social Networks for the identification of trends. Our methodology focuses on the utilization of semantics for determination of relevant networks within unstructured data. The Social Networks are examined from the perspective of structure and considered as a time series. Our metrics focus on the identification of influence and power among key players. This method is applied against a collection of Twitter messages and compared to historical market share trends of technologically-related topics. Through this work we demonstrate that structural qualities reflecting community dynamics can provide insight to the prediction of long-term trends. The goal of this work is to lend insight to the characterization of consumer behavior, particularly in the area of technology forecasting.
    Semantic Computing (ICSC), 2012 IEEE Sixth International Conference on; 01/2012
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    ABSTRACT: In this paper we analyze the success of startups in Germany by looking at the social network structure of their founders on the German-language business-networking site XING. We address two related research questions. First we examine university-wide networks, constructing alumni networks of 12 German universities, with the goal of identifying the most successful founder networks among the 12 universities. Second, we also look at individual actor network structure, to find the social network attributes of the most successful founders. We automatically collected the publicly accessible portion of XING, filtering people by attributes indicative of their university, and roles as founders, entrepreneurs, and CEOs. We identified 51,976 alumni, out of which 14,854 have entrepreneurship attributes. We also manually evaluated the financial success of a subsample of 80 entrepreneurs for each university. We found that universities, which are more central in the German university network, provide a better environment for students to found more and more successful startups. University networks whose alumni have a stronger “old-boys-network”, i.e. a larger share of their links with other alumni of their alma mater, are more successful as founders of startups. On the individual level the same holds true: the more links founders have with alumni of their university, the more successful their startup is. Finally, the absolute amount of networking matters, i.e. the more links entrepreneurs have, and the higher their betweenness in the online network of university alumni, the more successful they are.

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Jun 3, 2014