ALBERTO PEPE

I am the co-founder of Authorea, a collaborative word processor and repository for scientists. I am also a data consultant and a Research Associate at Harvard University, where I recently completed a Postdoc in Astrophysics. At Harvard, I was also a fellow of the Berkman Center for Internet & Society and an affiliate of the Institute for Quantitative Social Science. I hold a Ph.D. in Information Science from the University of California, Los Angeles with a dissertation on scientific collaboration networks. I live in Brooklyn, NY. I was born and raised in the small wine-making town of Manduria, in Puglia, Southern Italy. Contact me at alberto.pepe@gmail.com.
Regardless of their content and intended use, tweets (Twitter posts) often convey pertinent information about their author’s mood status. As such, tweets can be regarded as temporally-authentic microscopic instantiations of public mood state. The image above shows how the public mood, measured along six different emotional indicators using aggregate Twitter data, changed before, during and after the US presidential election on November 4, 2008.  
In this article - Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena -  I coauthored with Johan Bollen and Huina Mao, we perform a sentiment analysis of all public tweets broadcasted by Twitter users between August 1 and December 20, 2008. For every day in the timeline, we extract six dimensions of mood (tension, depression, anger, vigor, fatigue, confusion) using an extended version of the Profile of Mood States (POMS), a well-established psychometric instrument. We compare our results to fluctuations recorded by stock market and crude oil price indices and major events in media and popular culture, such as the U.S. Presidential Election of November 4, 2008 and Thanksgiving Day.
We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate 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.

Regardless of their content and intended use, tweets (Twitter posts) often convey pertinent information about their author’s mood status. As such, tweets can be regarded as temporally-authentic microscopic instantiations of public mood state. The image above shows how the public mood, measured along six different emotional indicators using aggregate Twitter data, changed before, during and after the US presidential election on November 4, 2008.  

In this article - Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena -  I coauthored with Johan Bollen and Huina Mao, we perform a sentiment analysis of all public tweets broadcasted by Twitter users between August 1 and December 20, 2008. For every day in the timeline, we extract six dimensions of mood (tension, depression, anger, vigor, fatigue, confusion) using an extended version of the Profile of Mood States (POMS), a well-established psychometric instrument. We compare our results to fluctuations recorded by stock market and crude oil price indices and major events in media and popular culture, such as the U.S. Presidential Election of November 4, 2008 and Thanksgiving Day.

We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate 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.

— 2 years ago