MotivationsPersonality Recognition is an emerging research field, that consists of the automatic classification of users' personality traits from multimedia data such as video, speeech and text. The applications of Personality Recognition range from Social Network Analysis to Opinion Mining/Sentiment Analysis, Automatic Behaviour Analysis, Security Systems, Mood Prediction, Deception Detection, Authorship Attribution, multimodal Human-Computer Interaction and many others. The lack of a widely accepted evaluation method in personality recognition makes hard to understand the State-of-the-art in this field. The main goals of the Workshop on Computational Personality Recognition 2014, that will be held in conjunction with ACMMM 2014 on November 7, are: 1) define a state-of-the-art in personality recognition; 2) release tools for future standard evaluations of Computational Personality Recognition tasks. Call for Papers/Participation to WorkshopThe organizers of the Workshop on Computational Personality Recognition 2014 invite contributions from researchers or teams working in: Data Mining, Multimedia Analysis,Computational Psychology, Natural Language Processing, Social Network Analysis, Sentiment Analysis, Opinion Mining, Mood Detection, Deception Detection,Information Extraction, Human-Computer Interaction,Authorship Attribution and other related areas. Organizers will provide two gold standard labelled datasets: - Transcriptions of video blogs: a dataset of texts and links to videos, labelled with personality. (Biel & Gatica-Perez 2013).- Mobile phones data labelled with personality types, collected inside the Friends and Family longitudinal study by MIT Human Dynamics group (Aharoni et al. 2011; Staiano et al. 2012 and de Montjoye et al. 2013).
The Workshop is structured in two tracks: one open shared task and one competition. Contributors are required to: - Track 1: competition. Organizers provide the datasets, the guidelines and scripts for evaluation. Contributors are required to: 1) develop their own system for personality type classification, that generates the predictions in the format required by the scripts;2) submit a short paper (max. 4 pages, including references) reporting all the information about features, resources and techniques used in the experiments, and a discussion of the results. 3) report predictions file while submitting the paper. Contributors are not allowed to add annotation levels using any type of external resource.- Track 2: open shared task. Organizers provide the datasets and the shared task guidelines. Contributors are required to: 1) use at least one of the datasets provided by the organizers; 2) submit a short paper (max. 4 pages, including references) reporting all the information about features, resources and techniques used in the experiments, and a discussion of the results. Contributors are allowed to add annotation levels using any type of external resource.
The Programme committee will evaluate and select the papers for publication on the basis of 1) performance, 2) clarity, 3) correctness, 4) meaningful comparison, 5) significance of the results, 6) soundness and replicability. Negative results will be considered as important as positive results. Selected papers will be presented as posters and published in the Workshop proceedings. The winner of the competition will be invited to do a demo session. The best papers will be selected for an extended journal publication. Task organizers will provide an introductory paper to summarize the results and describe the task, that will be published in the proceedings of ACMMM. The results of the workshop will be available to the public. More info about the details of the submission and the important dates on the website of the workshop: https://sites.google.com/site/wcprst/home/wcpr14 Programme CommitteeMichal Kosinski (Psychometrics Centre, University of Cambridge);Evgeny A Stepanov (University of Trento);Marco Guerini (FBK); Elia Bruni (CIMeC); Matteo Magnani (Uppsala University); Paolo Rosso (University of Valencia); Alastair Gill (King's College, London); Albert Ali Salah (Bogazici University); Scott Nowson (Xerox Research Center);Oya Aran (Idiap Research Institute); Yves-Alexandre de Montojye (MIT Media Lab); Louis-Philippe Morency (USC); Maja Pantic (Imperial College); David Lazer (Northesastern University); Matthias Mehl (Arizona University); Adrien Friggeri (Facebook Data Science); Walter Daelemans (University of Antwerp); Francisco Iacobelli (Northwestern University); Scott Nowson (Xerox research Center); Ben Verhoeven (University of Antwerp). Task OrganizersFabio Celli (university of Trento)Bruno Lepri (FBK)Joan-Isaac Biel (IDIAP)Daniel Gatica-Perez (IDIAP)Giuseppe Riccardi (University of Trento)Fabio Pianesi (FBK)
Fabio CelliPost DocDepartment of Information Engineering and Computer ScienceUniversity of Trentohttp://clic.cimec.unitn.it/fabio/
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