Andreas Kaltenbrunner

Statistical analysis of the social network and discussion threads in slashdot

We analyze the social network emerging from the user comment activity on the website Slashdot. The network presents common features of traditional social networks such as a giant component, small average path length and high clustering, but differs from them showing moderate reciprocity and neutral assortativity by degree. Using Kolmogorov-Smirnov statistical tests, we show that the degree distributions are better explained by log-normal instead of power-law distributions. We also study the structure of discussion threads using an intuitive radial tree representation. Threads show strong heterogeneity and self-similarity throughout the different nesting levels of a conversation. We use these results to propose a simple measure to evaluate the degree of controversy provoked by a post.

Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system

This paper provides an analysis of human mobility data in an urban area using the amount of available bikes in the stations of the community bicycle program Bicing in Barcelona. Based on data sampled from the operators website, it is possible to detect temporal and geographic mobility patterns within the city. These patterns are applied to predict the number of available bikes for any station some minutes/hours ahead. The predictions could be used to improve the bicycle program and the information given to the users via the Bicing website.

The structure of political discussion networks: a model for the analysis of online deliberation

This paper shows that online political discussion networks are, on average, wider and deeper than the networks generated by other types of discussions: they engage a larger number of participants and cascade through more levels of nested comments. Using data collected from the Slashdot forum, this paper reconstructs the discussion threads as hierarchical networks and proposes a model for their comparison and classification. In addition to the substantive topic of discussion, which corresponds to the different sections of the forum (such as Developers, Games, or Politics), we classify the threads according to structural features like the maximum number of comments at any level of the network (i.e. the width) and the number of nested layers in the network (i.e. the depth). We find that political discussion networks display a tendency to cluster around the area that corresponds to wider and deeper structures, showing a significant departure from the structure exhibited by other types of discussions. We propose using this model to create a framework that allows the analysis and comparison of different internet technologies for the promotion of political deliberation.

Description and Prediction of Slashdot Activity

We perform a statistical analysis of users reaction time to a new discussion thread in online debates on the popular news site Slashdot. First, we show with Kolmogorov-Smirnov tests that a mixture of two log-normal distributions combined with the circadian rhythm of the community is able to explain with surprising accuracy the reaction time of comments within a discussion thread. Second, this characterization allows to predict intermediate and long-term user behavior with acceptable precision. The prediction method is based on activity-prototypes, which consist of a mixture of two log-normal distributions, and represent the average activity in a particular region of the circadian cycle.

Core decomposition of uncertain graphs

Core decomposition has proven to be a useful primitive for a wide range of graph analyses. One of its most appealing features is that, unlike other notions of dense subgraphs, it can be computed linearly in the size of the input graph. In this paper we provide an analogous tool for uncertain graphs, i.e., graphs whose edges are assigned a probability of existence. The fact that core decomposition can be computed efficiently in deterministic graphs does not guarantee efficiency in uncertain graphs, where even the simplest graph operations may become computationally intensive. Here we show that core decomposition of uncertain graphs can be carried out efficiently as well. We extensively evaluate our definitions and methods on a number of real-world datasets and applications, such as influence maximization and task-driven team formation.

Communication Dynamics in Twitter During Political Campaigns: The Case of the 2011 Spanish National Election

The irruption of social media in the political sphere is generating repositories of “Big Data,” which can be mined to gain insights into communication dynamics. The research reported here relies on a large data set from Twitter to examine the activity, emotional content, and interactions of political parties and politicians during the campaign for the Spanish national elections in November 2011. The aim of this study is to investigate the adaptation of political parties to this new communication and organizational paradigm originating in the evolution of the Internet and online social networks. We analyze the reply and retweet networks of seven political parties with significant offline differences to assess their conversation and information diffusion patterns. We observe that political parties, and especially the major traditional parties, still tend to use Twitter just as a one-way flow communication tool. Moreover, we find evidence of a balkanization trend in the Spanish online political sphere, as observed in previous research for other countries.

Modeling the structure and evolution of discussion cascades

We analyze the structure and evolution of discussion cascades in four popular websites: Slashdot, Barrapunto, Meneame and Wikipedia. Despite the big heterogeneities between these sites, a preferential attachment (PA) model with bias to the root can capture the temporal evolution of the observed trees and many of their statistical properties, namely, probability distributions of the branching factors (degrees), subtree sizes and certain correlations. The parameters of the model are learned efficiently using a novel maximum likelihood estimation scheme for PA and provide a figurative interpretation about the communication habits and the resulting discussion cascades on the four different websites.

Societal Controversies in Wikipedia Articles

Collaborative content creation inevitably reaches situations where different points of view lead to conflict. We focus on Wikipedia, the free encyclopedia anyone may edit, where disputes about content in controversial articles often reflect larger societal debates. While Wikipedia has a public edit history and discussion section for every article, the substance of these sections is difficult to phantom for Wikipedia users interested in the development of an article and in locating which topics were most controversial. In this paper we present Contropedia, a tool that augments Wikipedia articles and gives insight into the development of controversial topics. Contropedia uses an efficient language agnostic measure based on the edit history that focuses on wiki links to easily identify which topics within a Wikipedia article have been most controversial and when.

Interactions of Cultures and Top People of Wikipedia from Ranking of 24 Language Editions

Wikipedia is a huge global repository of human knowledge that can be leveraged to investigate interwinements between cultures. With this aim, we apply methods of Markov chains and Google matrix for the analysis of the hyperlink networks of 24 Wikipedia language editions, and rank all their articles by PageRank, 2DRank and CheiRank algorithms. Using automatic extraction of people names, we obtain the top 100 historical figures, for each edition and for each algorithm. We investigate their spatial, temporal, and gender distributions in dependence of their cultural origins. Our study demonstrates not only the existence of skewness with local figures, mainly recognized only in their own cultures, but also the existence of global historical figures appearing in a large number of editions. By determining the birth time and place of these persons, we perform an analysis of the evolution of such figures through 35 centuries of human history for each language, thus recovering interactions and entanglement of cultures over time. We also obtain the distributions of historical figures over world countries, highlighting geographical aspects of cross-cultural links. Considering historical figures who appear in multiple editions as interactions between cultures, we construct a network of cultures and identify the most influential cultures according to this network.

Far from the eyes, close on the web: impact of geographic distance on online social interactions

Online friendship connections are often not representative of social relationships or shared interest between users, but merely provide a public display of personal identity. A better picture of online social behaviour can be achieved by taking into account the intensity of communication levels between users, yielding useful insights for service providers supporting this communication. Among the several factors impacting user interactions, geographic distance might be affecting how users communicate with their friends. While spatial proximity appears influencing how people connect to each other even on the Web, the relationship between social interaction and spatial distance remains unexplored. In this work we analyse the relationship between online user interactions and geographic proximity with a detailed study of a large Spanish online social service. Our results show that while geographic distance strongly affects how social links are created, spatial proximity plays a negligible role on user interactions. These findings offer new insights on the interplay between social and spatial factors influencing online user behaviour and open new directions for future research and applications.