Social media today provide an impressive amount of data about users and their societal interactions, thereby offering computer scientists, social scientists, economists, and statisticians many new opportunities for research exploration. Arguably one of the most interesting lines of work is that of forecasting future events and developments based on social media data, as we have recently seen in the areas of politics, finance, entertainment, market demands, health, etc.
But what can successfully be predicted and why? Since the first algorithms and techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains.
Better understanding the predictive power and limitations of social media is therefore of utmost importance, in order to, for example, avoid false expectations, misinformation or unintended consequences. Today, current methods and techniques are far from being well understood, and it is mostly unclear to what extent or under what conditions the different methods for prediction can be applied to social media. While there exists a respectable and growing amount of literature in this area, current work is fragmented, characterized by a lack of common evaluation approaches. Yet, this research seems to have reached a suficient level of interest and relevance to justify a dedicated special issue.
This special issue aims to shape a vision of important questions to be addressed in this field and fill the gaps in current research by soliciting presentations of early research on algorithms, techniques, methods and empirical studies aimed at the prediction of future or present events based on user generated content in social media.
To address this guiding theme the special issue will be articulated around, but not limited to, the following topics:
1. Politics, branding, and public opinion mining (e.g., electoral, market or stock market prediction).
2. Health, mood, and threats (e.g., epidemic outbreaks, social movements).
3. Methodological aspects (e.g., data collection, data sampling, privacy and data de-identification).
4. Success and failure case studies (e.g., reproducibility of previous research or selection of base-lines).
– Manuscript due date: June 1, 2012
– Decisions due: August 1, 2012
– Revised paper due: September 15, 2012
– Notification of acceptance: October 1, 2012
– Submission of final manuscript: October 31, 2012
– Publication date: late 2012 / early 2013 (tentative)
All submitted manuscripts should be original contributions and not be under consideration in any other venue.
Publication of an enhanced version of a previously published conference paper is possible if the review process determines that the revision contains significant enhancements, amplification or clarification of the original material. Any prior appearance of a substantial amount of a submission should be noted in the submission letter and on the title page.
Submissions must adhere to the Author Guidelines available at:
Detailed instructions will be announced later this year.
– Daniel Gayo-Avello [@PFCdgayo], University of Oviedo (Spain), firstname.lastname@example.org
– Panagiotis Takis Metaxas [@takis_metaxas], Wellesley College and Harvard University (USA), email@example.com
– Eni Mustafaraj [@enimust], Wellesley College (USA), firstname.lastname@example.org
– Markus Strohmaier [@mstrohm], Graz University of Technology (Austria), email@example.com
– Harald Schoen, University of Bamberg (Germany), firstname.lastname@example.org
– Peter Gloor [@pgloor], MIT (USA), email@example.com
Feel free to contact the guest editors if you have any question.