Stefan Nann

Bio: Stefan Nann is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Social network analysis & Semantic social network. The author has an hindex of 6, co-authored 9 publications receiving 247 citations. Previous affiliations of Stefan Nann include University of Cologne.

Web Science 2.0: Identifying Trends through Semantic Social Network Analysis

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 amounts 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.

Predictive Analytics On Public Data - The Case Of Stock Markets

Abstract: This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor’s 500 index (S&P 500) during a period from June 2011 to November 2011. To forecast price movements we filter messages by stocks, apply state-of-the-art sentiment analysis to message texts, and aggregate message sentiments to generate trading signals for daily buy and sell decisions. We evaluate prediction quality through a simple trading model considering real-world limitations like transaction costs or broker commission fees. Considering 833 virtual trades, our model outperformed the S&P 500 and achieved a positive return on investment of up to ~0.49% per trade or ~0.24% when adjusted by market, depending on supposed trading costs.

The Power of Alumni Networks - Success of Startup Companies Correlates With Online Social Network Structure of Its Founders

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.

Models and methods in social network analysis

Abstract: Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust’s Social Network Analysis: Methods and Applications, it is a collection of original articles by leading methodologists reviewing recent advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodeling, the analysis of diffusion through networks, the analysis of affiliation or “two-mode” networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphic techniques for exploring network data, and software for the analysis of social networks.

Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France

Abstract: The growing usage of social media by a wider audience of citizens sharply increases the possibility of investigating the web as a device to explore and track political preferences. In the present paper we apply a method recently proposed by other social scientists to three different scenarios, by analyzing on one side the online popularity of Italian political leaders throughout 2011, and on the other the voting intention of French Internet users in both the 2012 presidential ballot and the subsequent legislative election. While Internet users are not necessarily representative of the whole population of a country’s citizens, our analysis shows a remarkable ability for social media to forecast electoral results, as well as a noteworthy correlation between social media and the results of traditional mass surveys. We also illustrate that the predictive ability of social media analysis strengthens as the number of citizens expressing their opinion online increases, provided that the citizens act consistently...

A Critical Review of Centrality Measures in Social Networks

Abstract: Social networks are currently gaining increasing impact in the light of the ongoing growth of web-based services like facebook.com. One major challenge for the economically successful implementation of selected management activities such as viral marketing is the identification of key persons with an outstanding structural position within the network. For this purpose, social network analysis provides a lot of measures for quantifying a member’s interconnectedness within social networks. In this context, our paper shows the state of the art with regard to centrality measures for social networks. Due to strongly differing results with respect to the quality of different centrality measures, this paper also aims at illustrating the tremendous importance of a reflected utilization of existing centrality measures. For this purpose, the paper analyzes five centrality measures commonly discussed in literature on the basis of three simple requirements for the behavior of centrality measures.