ICWSM Dataset Sharing Service

As part of the ICWSM Data Sharing Initiative, ICWSM provides a hosting service for new datasets used by papers published in the proceedings of the annual ICWSM conference. All datasets are released as openly available community resources. Please see the instructions on the registration process in order to gain access to the datasets.

Available Datasets

This is the first year of the initiative and we are proud to present the list of participating papers from ICWSM 2012.

PaperDescription# of files# of tweets# of Facebook accounts# of entries# of Twitter users# of nodes# of edges
Z Luo, Miles O and T Wang. Opinion Retrieval in Twitter.Tweets tagged as relevant or irrelevant for 50 specific queries15000
L Chen, W Wang, M Nagarajan, S Wang and AP Sheth. Extracting Diverse Sentiment Expressions with Target-dependent Polarity from Twitter.Tweets describing movies and persons4426660
J Mahmud, J Nichols, and C Drews. Where is This Tweet From? Inferring Home Locations of Twitter Users.Geo tagged tweets from 100 top cities2100525919390
L Rossi and M Magnani. Conversation practices and network structure in Twitter.Tweets about the fifth edition of the popular TV show XFactor Italia222287
LM Aiello, M Deplano, R Schifanella, and G Ruffo. People are Strange when you're a Stranger: Impact and Influence of Bots on Social Networks.Social info from anobii.com76715851796541566369
J Park, M Cha, H Kim, and J Jeong. Managing Bad News in Social Media: A Case Study on Domino’s Pizza Crisis.Tweet collection for Domino's pizza crisis104645615829987
Y He, C Lin, W Gao, and KF Wong. Tracking Sentiment and Topic Dynamics from Social Media.Mozilla add on review data19300
D O'Callaghan, M Harrigan, J Carthy, and P Cunningham. Network Analysis of Recurring YouTube Spam Campaigns.Youtube comments data16466882
P Agarwal, R Vaithiyanathan, S Sharma, and G Shroff. Catching the Long-Tail: Extracting Local News Events from Twitter.Tweets that possibly describe a fire in a factory.35420751
FA Zamal, W Liu, and D Ruths. Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors.Tweet data for gender/age/political orientation labeled users9233653995340248
F Giglietto. If Likes Were Votes: An Empirical Study on the 2011 Italian Administrative Elections.Facebook profile of 104 Italian politicians1104

Obtaining Datasets

Download and sign the ICWSM Dataset Usage Agreement. Please note that this agreement gives you access to all ICWSM-published datasets. In it, you agree not to redistribute the datasets. Furthermore, ensure that, when using a dataset in your own work, you abide by the citation requests of the authors of the dataset used.

Email the signed agreement, as a PDF file, to dataset-request@icwsm.org. In the body of your email,

  1. Be clear that you are requesting access to the ICWSM datasets
  2. Include your name,
  3. your email address, and
  4. the name of your organization.

We will respond to your request with a URL, a username, and a password with which you can download the datasets. Please allow seven business days for a response.

Contact

If you have any questions or concerns regarding the terms of agreement, the datasets available, or need to report infringements on the terms of agreement, please contact Derek Ruths.





This page created on June 7, 2012
Last updated on June 7, 2012 03:41 EDT
Contact: Derek Ruths