Resources

We have created this resource page for detailed information on available datasets, iMotions, content analysis tools, and more from the CCDS, compiled by the Faculty Affiliates. 

Contact/Provider:  Jeff Hemsley        
Resource Type: Software
Description:  Twitter data collector  
Title: STACKS: Social Media Tracker, Analyzer, & Collector Toolkit at Syracuse           
Link: https://github.com/bitslabsyr/stack
Availability Date: Since August 2014

Limitations/Licensing Info: Open source

Contact/Provider: Josh Introne
Resource Type: Data
Description:  WebMD forum data (~5 years).  Includes 51 Health Exchanges, spanning a period from about 1/1/2009 - 8/15/2014.  Data includes all discussions, along with inferences about Emotional / Informational support exchange, core / peripheral members, inferred replies.  See Introne, J., Erickson, I., Semaan, B. & Goggins, S. Designing sustainable online support: Examining the effects of design change in 49 online health support communities. Journal of the Association for Information Science and Technology (2019).
Title: WebMD Health Exchanges        
Link: By request
Availability Date: May 2020
Limitations/Licensing Info: No license - iSchool only

Contact/Provider: Josh Introne          
Resource Type: Software
Description:  Web crawler for anti-vaxx web
Title: Anti-vaxx crawler
Link: https://github.com/MkZhang95/links_spider
Availability Date: May 2020  
Limitations/Licensing Info: Open source

Contact/Provider: Josh Introne          
Resource Type: Data
Description:  Anti-vaxx discussion data (3 forums)
Title: N/A
Link: By request
Availability Date: May 2020
Limitations/Licensing Info: No license - iSchool only

Contact/Provider: Josh Introne/Jeff Hemsley            
Resource Type: Data
Description:  COVID-19 Twitter Data:  Includes data from a long-running streaming API query, starting Jan. 30.  Set was expanded on Feb 16 to include Chinese hashtags.  Fully hydrated data
Title: COVID-19 Twitter Data
Link: By request
Availability Date: Ongoing
Limitations/Licensing Info: No license - iSchool only

Contact/Provider: Daniel Acuna / Lizhen Liang         
Resource Type: Software
Description:  Code for detecting biases in AI models: The repository includes code for detecting biases in AI models using two-alternative forced choice method and markov chain monty carlo and visualization showing how specifically word embedding models are biased, mentioned in: https://arxiv.org/abs/1912.10818
Title: Detecting biases in AI models   
Link: https://github.com/LiamLiang/Bias_AI
Availability Date: Since Jan 2020       
Limitations/Licensing Info: Open source