BibSonomy publications for /author/BollenTue Feb 07 00:00:00 CET 2012ARIST11-64Usage bibliometrics.442010dblp Tue Jan 24 12:21:50 CET 2012PLoS ONEmar3e4803Clickstream Data Yields High-Resolution Maps of Science42009analysis citation science toread scientometrics Background Intricate maps of science have been created from citation data to visualize the structure of scientific activity. However, most scientific publications are now accessed online. Scholarly web portals record detailed log data at a scale that exceeds the number of all existing citations combined. Such log data is recorded immediately upon publication and keeps track of the sequences of user requests (clickstreams) that are issued by a variety of users across many different domains. Given these advantages of log datasets over citation data, we investigate whether they can produce high-resolution, more current maps of science. Methodology Over the course of 2007 and 2008, we collected nearly 1 billion user interactions recorded by the scholarly web portals of some of the most significant publishers, aggregators and institutional consortia. The resulting reference data set covers a significant part of world-wide use of scholarly web portals in 2006, and provides a balanced coverage of the humanities, social sciences, and natural sciences. A journal clickstream model, i.e. a first-order Markov chain, was extracted from the sequences of user interactions in the logs. The clickstream model was validated by comparing it to the Getty Research Institute's Architecture and Art Thesaurus. The resulting model was visualized as a journal network that outlines the relationships between various scientific domains and clarifies the connection of the social sciences and humanities to the natural sciences. Conclusions Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the underrepresentation of the social sciences and humanities that is commonly found in citation data.Wed Jan 04 00:00:00 CET 2012ICWSMconf/icwsm/2011Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena.2011dblp Sun Jan 01 00:00:00 CET 2012CoRRPredicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Dataabs/1112.10512011dblp Wed Dec 21 21:59:59 CET 2011New York, NY, USAProceedings of the 20th ACM conference on Hypertext and hypermedia359--360HT '09The role of tag suggestions in folksonomies2009folksonomy power-law preferential-attachment tag-recommendation tagging Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. We present experimental results that show that the power law distribution forms regardless of whether or not tag suggestions are presented to the users.Mon Dec 19 00:00:00 CET 2011J. Comput. Science11-8Twitter mood predicts the stock market.22011dblp Mon Dec 19 00:00:00 CET 2011ICLSconf/icls/2010-2169-171Interactive drawing tools to support modeling of dynamic systems.2010dblp Tue Dec 13 00:00:00 CET 2011CSCLconf/cscl/2005418-422Cognitive tutoring of collaboration: developmental and empirical steps towards realization.2005dblp Tue Dec 06 11:55:36 CET 2011cite arxiv:1112.1051Comment: This paper includes 10 pages, 6 figures and 10 tablesPredicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Data2011prediction social_media Financial market prediction on the basis of online sentiment tracking has drawn a lot of attention recently. However, most results in this emerging domain rely on a unique, particular combination of data sets and sentiment tracking tools. This makes it difficult to disambiguate measurement and instrument effects from factors that are actually involved in the apparent relation between online sentiment and market values. In this paper, we survey a range of online data sets (Twitter feeds, news headlines, and volumes of Google search queries) and sentiment tracking methods (Twitter Investor Sentiment, Negative News Sentiment and Tweet & Google Search volumes of financial terms), and compare their value for financial prediction of market indices such as the Dow Jones Industrial Average, trading volumes, and market volatility (VIX), as well as gold prices. We also compare the predictive power of traditional investor sentiment survey data, i.e. Investor Intelligence and Daily Sentiment Index, against those of the mentioned set of online sentiment indicators. Our results show that traditional surveys of Investor Intelligence are lagging indicators of the financial markets. However, weekly Google Insight Search volumes on financial search queries do have predictive value. An indicator of Twitter Investor Sentiment and the frequency of occurrence of financial terms on Twitter in the previous 1-2 days are also found to be very statistically significant predictors of daily market log return. Survey sentiment indicators are however found not to be statistically significant predictors of financial market values, once we control for all other mood indicators as well as the VIX. Mon Dec 05 00:00:00 CET 2011CoRRThe Convergence of Digital-Libraries and the Peer-Review Processabs/cs/05040842005dblp Mon Dec 05 00:00:00 CET 2011CoRRHappiness is assortative in online social networksabs/1103.07842011dblp Mon Dec 05 00:00:00 CET 2011CoRRUsage Impact Factor: the effects of sample characteristics on usage-based impact metricsabs/cs/06101542006dblp Mon Dec 05 00:00:00 CET 2011CoRRBiologically Motivated Distributed Designs for Adaptive Knowledge Managementcs.IR/00020102000dblp Mon Dec 05 00:00:00 CET 2011CoRRDynamic Linking of Smart Digital Objects Based on User Navigation Patternscs.DL/04010292004dblp Mon Dec 05 00:00:00 CET 2011CoRRAn Architecture for the Aggregation and Analysis of Scholarly Usage Dataabs/cs/06051132006dblp Mon Dec 05 00:00:00 CET 2011CoRRModeling Computations in a Semantic Networkabs/0706.00222007dblp Mon Dec 05 00:00:00 CET 2011CoRRCo-Authorship Networks in the Digital Library Research Communityabs/cs/05020562005dblp Mon Dec 05 00:00:00 CET 2011CoRRThe Availability and Persistence of Web References in D-Lib Magazineabs/cs/05110772005dblp Mon Dec 05 00:00:00 CET 2011CoRRUsing RDF to Model the Structure and Process of Systemsabs/0709.11672007dblp Mon Dec 05 00:00:00 CET 2011CoRRInitial Experiences Re-Exporting Duplicate and Similarity Computation with an OAI-PMH aggregatorcs.DL/04010012004dblp