• Publications
  • Influence
Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena
TLDR
It is speculated that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.
A Principal Component Analysis of 39 Scientific Impact Measures
TLDR
The results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others.
Predicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Data
TLDR
This paper surveys a range of online data sets and sentiment tracking methods and compares their value for financial prediction of market indices such as the Dow Jones Industrial Average, trading volumes, and market volatility, as well as gold prices.
More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior
TLDR
There is a statistically significant association between tweets that mention a candidate for the U.S. House of Representatives and his or her subsequent electoral performance, and this finding persists even when controlling for incumbency, district partisanship, media coverage of the race, time, and demographic variables such as the district's racial and gender composition.
Happiness Is Assortative in Online Social Networks
TLDR
It is shown that the general happiness, or subjective well-being, of Twitter users, as measured from a 6-month record of their individual tweets, is indeed assortative across the Twitter social network.
How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations
TLDR
The volume of Twitter mentions is statistically correlated with arXiv downloads and early citations just months after the publication of a preprint, with a possible bias that favors highly mentioned articles.
Twitter Mood as a Stock Market Predictor
TLDR
This research presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually cataloging and cataloging individual tweets.
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