Competition

Introduction

As a result of the ongoing Coronavirus disease 2019 (COVID-19) pandemic, our daily life routines and behavior patterns changed drastically not only offline but also online. One example of such a change is the change in the reading patterns on Wikipedia and Reddit [1,2]. COVID-19 has also been a hot topic on other social media platforms such as Facebook, Twitter, or YouTube. 

To understand the information spreading mechanisms during the COVID-19 pandemic, in this challenge, we focus on Twitter. Twitter is an online social network where users can follow each other and share information using short text posts called tweets. The platform offers a function to retweet a tweet, which means sharing it with your followers without any change. Retweeting is a popular function and it has also found his way in other online social networks such as Weibo. Retweeting can be seen as amplifying the spread of original content and thus retweet prediction is a crucial task when studying information spreading processes. As such, understanding retweet behavior is useful and has many practical applications, e.g. political audience design [3,4], fake news spreading and tracking [5,6], health promotion [7], mass emergency management [8], etc. Modeling retweet behavior has been an active research area and is also especially important during times of crisis, such as the current COVID-19 pandemic. 

The retweet prediction task in the challenge is based on the TweetsCOV19 dataset --- a publicly available dataset containing more than 8 million COVID-19-related tweets, spanning the period October 2019 to April 2020.

The COVID-19 Retweet Prediction Challenge is part of the CIKM2020 AnalytiCup and the winners will be invited to present their solutions during the *online* AnalytiCup Workshop in October 2020. 

Timeline 

• 01.07.20 Contest and Phase 1 Begin (Validation Leaderboard opens)
• 15.08.20 Phase 2 Begin (Testing Leaderboard opens)
• 31.08.20 Last Shot & Contest End
• 01.09.20 Semi-Finalists Announcement (top six teams on the Testing Leaderboard)
• 01.10.20 Report & Code Due
• 20.10.20 Winners Announcement

Prizes and Sponsors

The winners of the COVID-19 Retweet Prediction Challenge await non-cash prizes worth 2.500€ provided by L3S Research Center, University of Hannover, Germany. The prizes will be distributed among the participants as follows:

• The 1st Place receives a non-cash prize equivalent of EUR1,200 (~USD1,350)*
• The 2nd Place receives a non-cash prize equivalent of EUR800 (~USD900)*
• The 3rd Place receives a non-cash prize equivalent of EUR500 (~USD560)*

*In order to be eligible for any award, the semi-finalists are required to submit the code and solution report (4 pages in ACM format) to the organizers by the stipulated deadline. The submitted codes and reports may be inspected to check the validity of the solution. The reports will eventually be made publicly available on the CIKM conference website.

Prizes will be awarded in the form of vouchers. L3S Research Center reserves the right not to award some of or all the prizes if the competition criteria are not met.

The complete list of sponsors includes:
GESIS – Leibniz Institute for the Social Sciences, Germany
Chongqing University of Technology, China
Heinrich-Heine-University Düsseldorf, Germany

Organizers 

Dimitar Dimitrov, GESIS – Leibniz Institute for the Social Sciences, Germany
Xiaofei Zhu, Chongqing University of Technology, China

Questions?

• Please first go through all the pages in this competition for complete information.
• If you have further questions, please post them on the Forum tab.

We wish you all the best!

Acknowledgments

We wouldn't be here without the help of the following people:

• Erdal Baran - helped with preparing the data
• Zhanwang Peng - helped to test the competition on CodaLab 

References 

[1] Gozzi, N., Tizzani, M., Starnini, M., Ciulla, F., Paolotti, D., Panisson, A. and Perra, N., 2020. Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia. arXiv preprint arXiv:2006.06446. 

[2] Ribeiro, M.H., Gligorić, K., Peyrard, M., Lemmerich, F., Strohmaier, M. and West, R., 2020. Sudden Attention Shifts on Wikipedia Following COVID-19 Mobility Restrictions. arXiv preprint arXiv:2005.08505.

[3] Stieglitz, S. and Dang-Xuan, L., 2012, January. Political communication and influence through microblogging--An empirical analysis of sentiment in Twitter messages and retweet behavior. In 2012 45th Hawaii International Conference on System Sciences (pp. 3500-3509). IEEE.

[4] Kim, E., Sung, Y. and Kang, H., 2014. Brand followers’ retweeting behavior on Twitter: How brand relationships influence brand electronic word-of-mouth. Computers in Human Behavior, 37, pp.18-25.

[5] Lumezanu, C., Feamster, N. and Klein, H., 2012, May. # bias: Measuring the tweeting behavior of propagandists. In Sixth International AAAI Conference on Weblogs and Social Media.

[6] Vosoughi, S., Roy, D. and Aral, S., 2018. The spread of true and false news online. Science, 359(6380), pp.1146-1151.

[7] Chung, J.E., 2017. Retweeting in health promotion: Analysis of tweets about Breast Cancer Awareness Month. Computers in Human Behavior, 74, pp.112-119.

[8] Kogan, M., Palen, L. and Anderson, K.M., 2015, February. Think local, retweet global: Retweeting by the geographically-vulnerable during Hurricane Sandy. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (pp. 981-993).