Nov 18, 2017 in New Orleans, USA

IEEE International Conference on Data Mining

Full Paper Submission Deadline: Aug 7, 2017
8:30 - 8:40 Welcome and opening Remarks
8:40 - 9:40 Keynote: Actionable Data Science: Revolutionizing Political Engagement and Polling
9:40 - 10:00 Measuring Network Structure Metrics as a Proxy for Socio-political Activity in Social Media
10:00 - 10:20 Coffee break
10:20 - 10:40 Us and Them: Adversarial Politics on Twitter
10:40 - 11:00 The influence of National leaders speech in Twitter on 2015 Venezuelan Parliamentary Elections
11:00 - 11:20 Controversy Detection using Reactions on Social Media
11:20 - 1:30 Lunch Break

 Call For Papers

Political analysis may once have depended entirely on subjective human evaluation of candidates and their spoken and written words, and on non‐scientific measurements of the electorate. With the increasing availability of data on current and prior political events, it is possible to add meaningful data-driven attributes to political analysis and forecasting. This type of analysis is used by political entities to understand their electorate, and by the electorate to understand and evaluate their political entities. Big Data collected from internet-of-things devices, online social networks, large‐scale surveys, search engine queries, historical events, news archives, and other sources can materially improve the quality of this analysis. This applies to fomenting and forecasting political unrest, automating or assisting in the fact-checking of political news and speech, predicting election outcomes, as recent work on empirically determining tipping points in influencing public opinion has shown.

Marketing companies and election consultants have long used sophisticated polling techniques in order to determine and shape public opinion so that candidates can use their findings to their advantage. In the last decade, however, we have seen well‐known applications of large-scale data analysis in politics, with mixed success. In 2008, President Obama’s campaign effectively monitored and leveraged social media as an important part of his campaign strategy. In 2016, Donald Trump used micro-targeting to identify and influence small groups of voters with tailored content. Meanwhile, many of the data-driven election forecasts fell short in predicting the outcome of the election. Subsequent to the 2016 US election, interest in automated or assisted fact-checking has grown to combat what is perceived as a problem with factually incorrect news and political speech.

 Topics of Interest

There have been many attempts to utilize data mining algorithms and tools in advertising, financial services, medical applications and others, but rigorous discussion of Big Data techniques in politics have tended to be closely guarded. This workshop aims at bringing together researchers from interdisciplinary areas and strengthens collaboration between the political science and data mining communities in understanding the contemporary use of Big Data techniques in politics. We encourage a useful exchange of ideas, techniques and datasets between researchers, political practitioners, social entrepreneurs, and corporate representatives through the workshop. Possible topics of interest include, but are not limited to:

  • Political dataset collection, dimensionality reduction, cleaning, and processing
  • Automated or assisted fact-checking of news articles and political speech, and identification of “fake news"
  • Applications of Big Data analytics to election campaigns
  • Sentiment analysis to predict political opinions
  • Social network analysis as a tool for political influence and prediction
  • Data-oriented innovations in politics
  • Case studies of data mining tools for politics

Actionable Data Science: Revolutionizing Political Engagement and Polling

Mike Polyakov has served as the VP of Data Science at since 2014. Crowdpac strives to make it easier for citizens to learn about politics, run for office and support political candidates that match their priorities and beliefs. Previously, Mike was Research Director at California Common Sense, a government transparency-focused non-profit and a research software engineer at Lockheed Martin. He holds a PhD from UC Berkeley in political science and a BA and ME in computer science from Cornell University.


We welcome two different types of publications: regular research papers and application abstract papers. The page limit for all papers is 6 pages in the standard IEEE 2-column format (see the template), including the bibliography and any possible appendices. All papers must be formatted according to the IEEE Computer Society proceedings manuscript style, following IEEE ICDM 2017 submission guidelines available at the conference webpage. Papers should be submitted in PDF format, electronically, using the CyberChair submission system.

Every workshop paper must have at least one paid registration in order to be published. Accepted papers will be included in the IEEE ICDM 2017 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. The workshop proceedings will be in a CD separated from the CD of the main conference. The CD is produced by IEEE Conference Publishing Services (CPS).

Three program committee members will evaluate every submission. In order to offer feedback from technical as well as subject-matter experts, at least one of these three members is from the political science community.

Important Dates

  • Workshop paper submissions: August 7, 2017
  • Workshop paper notifications: September 7, 2017
  • Camera-ready deadline for the final version of accepted papers: September 15, 2017
  • Workshop date: November 18th 2017

Program Chair

Tonya Custis

Thomson Reuters - Center for Cognitive Computing

Program Committee

Ahmed Farahat

Hitachi Labs

Amir Hajian

Thomson Reuters Labs

Cliff Young


Denilson Barbosa

University of Alberta

Frank Schilder

Thomson Reuters

Luna Feng

Thomson Reuters Labs

Workshop Chairs

Kenneth Ellis

CTO, Reuters News Agency

Tonya Custs

Thomson Reuters - Center for Cognitive Computing

Khaled Ammar

Thomson Reuters - Center for Cognitive Computing

Brian Ulicny

Thomson Reuters Labs

Amir Hajian

Thomson Reuters Labs

Luna Feng

Thomson Reuters Labs

Accepted Papers

Controversy Detection using Reactions on Social Media

Sriteja Allaparthi, Prakhar Pandey, and Vikram Pudi

Us and Them: Adversarial Politics on Twitter

Anna Guimarães, Liqiang Wang, and Gerhard Weikum


The Roosevelt New Orleans

130 Roosevelt Way, New Orleans, Louisiana, 70112, USA

+1-504-648 1200


Khaled Ammar

Khaled DOT ammar@thomsonreuters DOT com