Event attendance classification in social media

Abstract Popular events are well reflected on social media, where people share their feelings and discuss their experiences. In this paper, we investigate the novel problem of exploiting the content of non-geotagged posts on social media to infer the users’ attendance of large events in three temporal periods: before, during and after an event. We detail the features used to train event attendance classifiers and report on experiments conducted on data from two large music festivals in the UK, namely the VFestival and Creamfields events. Our classifiers attain very high accuracy with the highest result observed for the Creamfields festival ( ∼ 91% accuracy at classifying users that will participate in the event). We study the most informative features for the tasks addressed and the generalization of the learned models across different events. Finally, we discuss an illustrative application of the methodology in the field of transportation.

[1]  Eugenio Cesario,et al.  SMA4TD: A social media analysis methodology for trajectory discovery in large-scale events , 2017, Online Soc. Networks Media.

[2]  Dongwon Lee,et al.  @Phillies Tweeting from Philly? Predicting Twitter User Locations with Spatial Word Usage , 2012, 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

[3]  Richard O. Sinnott,et al.  Estimating micro-populations through social media analytics , 2017, Social Network Analysis and Mining.

[4]  Libo Li,et al.  Predicting online invitation responses with a competing risk model using privacy-friendly social event data , 2018, Eur. J. Oper. Res..

[5]  Zheng Wang,et al.  Learn to Recommend Local Event Using Heterogeneous Social Networks , 2016, APWeb.

[6]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[7]  Rong Du,et al.  Predicting activity attendance in event-based social networks: content, context and social influence , 2014, UbiComp.

[8]  Kyumin Lee,et al.  You are where you tweet: a content-based approach to geo-locating twitter users , 2010, CIKM.

[9]  Zhen Qin,et al.  A Scalable Approach for Periodical Personalized Recommendations , 2016, RecSys.

[10]  Rodrygo L. T. Santos,et al.  Context-Aware Event Recommendation in Event-based Social Networks , 2015, RecSys.

[11]  Eugenio Cesario,et al.  Analyzing social media data to discover mobility patterns at EXPO 2015: Methodology and results , 2016, 2016 International Conference on High Performance Computing & Simulation (HPCS).

[12]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[13]  Charu C. Aggarwal,et al.  A Survey of Text Classification Algorithms , 2012, Mining Text Data.

[14]  Tobias Preis,et al.  Quantifying crowd size with mobile phone and Twitter data , 2015, Royal Society Open Science.

[15]  Dimitrios Efthymiou,et al.  Use of Social Media for Transport Data Collection , 2012 .

[16]  Mudhakar Srivatsa,et al.  When twitter meets foursquare: tweet location prediction using foursquare , 2014, MobiQuitous.

[17]  Yu Liu,et al.  The promises of big data and small data for travel behavior (aka human mobility) analysis , 2016, Transportation research. Part C, Emerging technologies.

[18]  Eugenio Cesario,et al.  Following soccer fans from geotagged tweets at FIFA World Cup 2014 , 2015, 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM).

[19]  Omer Levy,et al.  Improving Distributional Similarity with Lessons Learned from Word Embeddings , 2015, TACL.

[20]  Xiaomei Zhang,et al.  Who Will Attend? -- Predicting Event Attendance in Event-Based Social Network , 2015, 2015 16th IEEE International Conference on Mobile Data Management.

[21]  Michel Ballings,et al.  The added value of Facebook friends data in event attendance prediction , 2016, Decis. Support Syst..

[22]  M. Williams,et al.  Knowing the Tweeters: Deriving Sociologically Relevant Demographics from Twitter , 2013 .

[23]  Craig MacDonald,et al.  Enhancing Sensitivity Classification with Semantic Features Using Word Embeddings , 2017, ECIR.

[24]  Cecilia Mascolo,et al.  The Call of the Crowd: Event Participation in Location-Based Social Services , 2014, ICWSM.

[25]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[26]  Carlo Ratti,et al.  Geo-located Twitter as proxy for global mobility patterns , 2013, Cartography and geographic information science.

[27]  Hong Yang,et al.  Collaborative Social Group Influence for Event Recommendation , 2016, CIKM.

[28]  Eleonora D'Andrea,et al.  Real-Time Detection of Traffic From Twitter Stream Analysis , 2015, IEEE Transactions on Intelligent Transportation Systems.

[29]  Aytug Onan,et al.  A machine learning based approach to identify geo-location of Twitter users , 2017, ICC.

[30]  Jeffrey Nichols,et al.  Home Location Identification of Twitter Users , 2014, TIST.

[31]  Tsvi Kuflik,et al.  The potential of social media in delivering transport policy goals , 2014 .

[32]  Qin Lv,et al.  Event Organization 101: Understanding Latent Factors of Event Popularity , 2017, ICWSM.

[33]  Marios D. Dikaiakos,et al.  Users key locations in online social networks: identification and applications , 2016, Social Network Analysis and Mining.

[34]  Richard O. Sinnott,et al.  Estimating crowd sizes through social media , 2016, 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[35]  Daniele Quercia,et al.  Recommending Social Events from Mobile Phone Location Data , 2010, 2010 IEEE International Conference on Data Mining.

[36]  D. Ruths,et al.  Social media for large studies of behavior , 2014, Science.

[37]  Qin Lv,et al.  Hybrid EGU-based group event participation prediction in event-based social networks , 2017, Knowl. Based Syst..

[38]  Susan Grant-Muller,et al.  The Impact of Social Media Usage on Transport Policy: Issues, Challenges and Recommendations☆ , 2014 .

[39]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[40]  Mufti Mahmud,et al.  Advances in Crowd Analysis for Urban Applications Through Urban Event Detection , 2018, IEEE Transactions on Intelligent Transportation Systems.

[41]  Li Guo,et al.  CPMF: A collective pairwise matrix factorization model for upcoming event recommendation , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[42]  Sheila Kinsella,et al.  "I'm eating a sandwich in Glasgow": modeling locations with tweets , 2011, SMUC '11.

[43]  Laurence T. Yang,et al.  Event recommendation in social networks based on reverse random walk and participant scale control , 2018, Future Gener. Comput. Syst..

[44]  Craig MacDonald,et al.  Exploring Social Media for Event Attendance , 2017, 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[45]  Masnizah Mohd,et al.  Sentiment Lexicon Interpolation and Polarity Estimation of Objective and Out-Of-Vocabulary Words to Improve Sentiment Classification on Microblogging , 2014, PACLIC.

[46]  Shaowen Wang,et al.  Mapping the global Twitter heartbeat: The geography of Twitter , 2013, First Monday.

[47]  Alyson G. Wilson,et al.  Twitter Geolocation , 2018, ACM Trans. Knowl. Discov. Data.