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Twitter mood predicts the stock market
Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena
- J. Bollen, Huina Mao, A. Pepe
- EconomicsInternational Conference on Web and Social Media
- 8 November 2009
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.
Predicting Financial Markets: Comparing Survey,News, Twitter and Search Engine Data
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.
Loose tweets: an analysis of privacy leaks on twitter
- Huina Mao, Xin Shuai, Apu Kapadia
- Computer ScienceWorkshop on Privacy in the Electronic Society
- 17 October 2011
The nature of privacy leaks on Twitter is characterized to gain an understanding of what types of private information people are revealing on it and automatic classifiers are built to detect incriminating tweets for these three topics in real time in order to demonstrate the real threat posed to users by, e.g., burglars and law enforcement.
Happiness Is Assortative in Online Social Networks
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.
Twitter Mood as a Stock Market Predictor
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.
Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data
Automatic Construction of Financial Semantic Orientation Lexicon from Large-Scale Chinese News Corpus
The research has shown the efficacy of relying the market to construct financial semantic orientation lexicon automatically, and the baseline methodology is language-independent, and can be easily extended to foreign languages.
Quantifying the effects of online bullishness on international financial markets
A simple, direct and unambiguous indicator of online investor sentiment, which is based on Twitter updates and Google search queries, is developed and it is observed that high Twitter bullishness predicts increases in stock returns, with these then returning to their fundamental values.
Quantifying socio-economic indicators in developing countries from mobile phone communication data: applications to Côte d’Ivoire
The CallRank indicator is introduced to quantify the relative importance of an area on the basis of call records, and it is shown that a region’s ratio of in- and out-going calls can predict its income level.