Computational methods to gauge investor sentiment from commonly used online data sources that rely on machine learning classifiers and lexicons have shown considerable promise, but suffer from measurement and classification errors. In our work, we develop a simple, direct and unambiguous...

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...

We examine the quality of information and communication in online investment discussion boards. We show that positivity bias and skewed risk/reward assessments, exacerbated by the insular nature of the community and its social structure, contribute to underperforming investment advice and...

Roughly one in ten Americans move every year, bringing significant social and economic impact to both the places they move from and places they move to. We show that migration intent mined from internet search queries can forecast domestic migration and provide new insights beyond government...

Is social media a valid indicator of political behavior? We answer this question using a random sample of 537,231,508 tweets from August 1 to November 1, 2010 and data from 406 competitive U.S. congressional elections provided by the Federal Election Commission. Our results show that the...

This paper proposes a framework to analyze the interdisciplinary collaboration in a coauthorship network from a meso perspective using topic modeling: (1) a customized topic model is developed to capture and formalize the interdisciplinary feature; and (2) the two algorithms Diversity Subgraph...

The peer-review process, in its present form, has been repeatedly criticized. Of the many critiques ranging from publication delays to referee bias, this paper will focus specifically on the issue of how submitted manuscripts are distributed to qualified referees. Unqualified referees, without...

The detection of communities in large social networks is receiving increasing attention in a variety of research areas. Most existing community detection approaches focus on the topology of social connections (e.g., coauthor, citation, and social conversation) without considering their topic and...

We investigate how the benefits of the TeraGrid supercomputing infrastructure are distributed across the scientific community. Do mostly high-impact scientists benefit from the TeraGrid? Are some scientific domains more strongly represented than others in TeraGrid-supported work? To answer these...