ACL 2014 Workshop on Latent Attribute Prediction in Social Media

Event Notification Type: 
Call for Papers
Baltimore Marriott Waterfront
Friday, 27 June 2014
Submission Deadline: 
Friday, 21 March 2014



ACL 2014 Workshop on Latent Attribute Prediction in Social Media

Collocated with ACL 2014
Main conference: June 23-25, 2014
Workshop: June 27, 2014

Paper Submission Deadline: March 21, 2014 (11:59pm PST)

Workshop Organizers:
Svitlana Volkova (Johns Hopkins University, USA)
Benjamin Van Durme (Johns Hopkins University, USA)
David Yarowsky (Johns Hopkins University, USA)

Keynote speakers:
- Derek Ruth, McGill University
- Henry Kautz, University of Rochester

There are many important social science questions and commercial applications that are impacted by the large amounts of diverse personalized data emerging from social media. These data can reveal user interests, preferences and opinions, as well as trends and activity patterns for companies and their products.

The automatic prediction of latent attributes from discourse in social media includes topics such as:
- inferring user/customer demographic profiles (gender, age, religion, social status, race, ethnicity, origin),
- predicting user interests (sports, movies) and preferences (political favorites or product likes), personality,
- classifying sentiment, emotional states (onset of depression) and opinions held by an author,
- analyzing general trends and influence for companies and products.

Important Dates:
All deadlines are calculated at 11:59pm (PST/GMT -7 hours)
21 March 2014: Workshop Paper Due Date
11 April 2014: Notification of Acceptance
28 April 2014: Camera-ready papers due (firm deadline)
27 June 2014: Workshop Date

Call for Papers:
We invite original and unpublished research papers on all topics related to text-driven latent attribute prediction in social media, including but not limited to the topics listed below:

- dynamic and streaming nature of social media data;
- joint latent attribute prediction (e.g., age together with political preference);
- multi-relational aspects of social media data (e.g., networks of friends, followers, user mentions, etc.);
- generalization of the existing models for text-driven author attribute prediction;
- scalability to other understudied languages and dialects in social media;
- data collection, sharing and labeling biases for personal analytics in social media;
- language variation in social media based on user attributes (e.g. gender, age and native language);
- mood, sentiment, emotion and opinion analysis of authors in social media;
- emotional states, distress, mental condition classification from communications in social networks;
- community and network structure analysis for entity attribute prediction;
- security, identity and privacy issues for personal analytics in social media.

Papers should follow the ACL long paper format.
START submission link:

Program Committee:
Abigail Jacobs (University of Colorado Boulder, USA)
Alessandro Moschitti (QCRI, Qatar)
Allan Ritter (Carnegie Melon University, USA)
Aron Culotta (Illinois Institute of Technology, USA)
Chris Dyer (Carnegie Melon University, USA)
Delip Rao (GlassDoor, USA)
Derek Ruth (McGill University, Canada)
Dong Nguyen University of Twente, Netherlands)
Eugene Kharitonov (Yandex, Russia)
Francisco Iacobelli (Northeastern Illinois University, USA)
Glen Coppersmith (Johns Hopkins University, USA)
Ilia Chetviorkin (Lomonosov Moscow State University, Russia)
Jacob Eisenstein (Georgia Institute of Technology, USA)
John Henderson (MITRE, USA)
Mark Dredze (Johns Hopkins University, USA)
Meg Mitchel (Microsoft Research, USA)
Michael Paul (Johns Hopkins University, USA)
Michael Gamon (Microsoft Research, USA)
Patrick Pantel (Microsoft Research, USA)
Pavel Braslavski (KonturLabs, Russia)
Pavel Sergyurkov (Yandex, Russia)
Philip Resnik (University of Maryland, USA)
Rebecca Knowles (Johns Hopkins University, USA)
Saif Mohammad (National Research Council, Canada)
Silviu-Petru Cucerzan (Microsoft Research, USA)
Vasileios Lampos University of Sheffield, UK)