Crowd or Hubs: information diffusion patterns in online social networks in disasters

Abstract The objective of this paper is to investigate the role of different types of users in the diffusion of situational information through online social networks in disasters. In particular, this paper investigates the influence of two types of users: crowd (regular users) and hubs (users with a large number of followers) on the speed and magnitude of information propagation. Effective and efficient disaster response requires rapid dissemination of situational information to improve situation awareness, save lives, and quickly repair damages. The use of social media, such as Twitter, has gained popularity for spreading the situational information in disasters. Little is known, however, regarding the underlying diffusion mechanisms influencing the speed and magnitude of spreading disaster-related information on social media. To address this gap, using tweets related to Hurricane Harvey, this study examined the role of hubs and crowd and the influence of different features such as theme, hashtag, media, users’ location, and the intervention timing of online users on the speed and magnitude of information spread. The results compare the differences in the speed and magnitude of information spread between two diffusion patterns: crowd diffusion (less than 2% retweets from hubs) and mixed diffusion (more than 2% retweets from hubs). The findings suggest that both diffusion patterns can achieve high speed and high magnitude in terms of information diffusion for trending tweets with different features. For mixed diffusion, the speed and magnitude of the tweets are governed by the activities of hubs. The results also show that early intervention of hubs increases the speed of information propagation. Also, in the crowd diffusion, information spread is influenced by both crowd and hubs whose retweets cause tipping points in the information diffusion process at different points of time. The findings imply intervention strategies to better disseminate situational information in disasters.

[1]  Andrea Marchetti,et al.  Predictability or Early Warning: Using Social Media in Modern Emergency Response , 2016, IEEE Internet Comput..

[2]  Jiebo Luo,et al.  Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter , 2016, ICWSM.

[3]  Firoj Alam,et al.  CrisisMMD: Multimodal Twitter Datasets from Natural Disasters , 2018, ICWSM.

[4]  Michael Grossniklaus,et al.  Situation monitoring of urban areas using social media data streams , 2016, Inf. Syst..

[5]  Daniel G. Goldstein,et al.  The structure of online diffusion networks , 2012, EC '12.

[6]  Yang Yang,et al.  Exploring the emergence of influential users on social media during natural disasters , 2019, International Journal of Disaster Risk Reduction.

[7]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[8]  Marina Kogan,et al.  Developing and Evaluating Annotation Procedures for Twitter Data during Hazard Events , 2018, LAW-MWE-CxG@COLING.

[9]  Sarah Vieweg,et al.  Processing Social Media Messages in Mass Emergency , 2014, ACM Comput. Surv..

[10]  Tomasz Bednarz,et al.  Image Classification to Support Emergency Situation Awareness , 2016, Front. Robot. AI.

[11]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[12]  Tao Cheng,et al.  Event Detection using Twitter: A Spatio-Temporal Approach , 2014, PloS one.

[13]  Chao Fan,et al.  Establishing a framework for disaster management system-of-systems , 2018, 2018 Annual IEEE International Systems Conference (SysCon).

[14]  송태민.  Social Big Data 기반 보건의료 연구방법론 , 2013 .

[15]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[16]  Jie Yin,et al.  Using Social Media to Enhance Emergency Situation Awareness , 2012, IEEE Intelligent Systems.

[17]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[18]  J. Lawless.  Negative binomial and mixed Poisson regression , 1987 .

[19]  Marina Kogan,et al.  Think Local, Retweet Global: Retweeting by the Geographically-Vulnerable during Hurricane Sandy , 2015, CSCW.

[20]  K. J. Ray Liu,et al.  Graphical Evolutionary Game for Information Diffusion Over Social Networks , 2013, IEEE Journal of Selected Topics in Signal Processing.

[21]  Maurizio Tesconi,et al.  Nowcasting of Earthquake Consequences Using Big Social Data , 2017, IEEE Internet Computing.

[22]  Guilin Qi,et al.  Semantic Web and Web Science , 2013, Springer Proceedings in Complexity.

[23]  Chunxiao Jiang,et al.  The value strength aided information diffusion in online social networks , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[24]  J. Goldenberg,et al.  The Role of Hubs in the Adoption Process , 2009 .

[25]  Cheng Zhang,et al.  A System Analytics Framework for Detecting Infrastructure-Related Topics in Disasters Using Social Sensing , 2018, EG-ICE.

[26]  Carter T. Butts,et al.  A cross-hazard analysis of terse message retransmission on Twitter , 2015, Proceedings of the National Academy of Sciences.

[27]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[28]  Ali Mostafavi,et al.  A graph‐based method for social sensing of infrastructure disruptions in disasters , 2019, Comput. Aided Civ. Infrastructure Eng..

[29]  Yamir Moreno,et al.  The role of hidden influentials in the diffusion of online information cascades , 2013, EPJ Data Science.

[30]  Ali Mostafavi,et al.  A Hybrid Machine Learning Pipeline for Automated Mapping of Events and Locations From Social Media in Disasters , 2020, IEEE Access.

[31]  Carter T. Butts,et al.  What it Takes to Get Passed On: Message Content, Style, and Structure as Predictors of Retransmission in the Boston Marathon Bombing Response , 2015, PloS one.

[32]  Andrea Marchetti,et al.  EARS (earthquake alert and report system): a real time decision support system for earthquake crisis management , 2014, KDD.

[33]  Martha G. Russell,et al.  Transparency in Social Media , 2015, Computational Social Sciences.

[34]  Sean Fitzhugh,et al.  Terse message amplification in the Boston bombing response , 2014, ISCRAM.

[35]  J. Fowler,et al.  Rapid assessment of disaster damage using social media activity , 2016, Science Advances.

[36]  Cheng Zhang,et al.  Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management , 2021, Int. J. Inf. Manag..

[37]  Nigel G. Bean,et al.  The Nature and Origin of Heavy Tails in Retweet Activity , 2017, WWW.

[38]  Cécile Favre,et al.  Information diffusion in online social networks: a survey , 2013, SGMD.

[39]  Xinbing Wang,et al.  The Value Strength Aided Information Diffusion in Socially-Aware Mobile Networks , 2016, IEEE Access.

[40]  Aoying Zhou,et al.  Impact of Multimedia in Sina Weibo: Popularity and Life Span , 2012, CSWS.

[41]  Qunying Huang,et al.  Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study , 2016 .

[42]  Stefan Stieglitz,et al.  Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior , 2013, J. Manag. Inf. Syst..

[43]  James Caverlee,et al.  Text vs. images: on the viability of social media to assess earthquake damage , 2013, WWW.

[44]  Maurizio Tesconi,et al.  CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing , 2018, Information Systems Frontiers.

[45]  Yili Hong,et al.  Modeling Twitter Engagement in Real-World Events , 2017, HICSS.

[46]  Jason J. Jung,et al.  Social big data: Recent achievements and new challenges , 2015, Information Fusion.

[47]  Michael Grossniklaus,et al.  An evaluation of the run-time and task-based performance of event detection techniques for Twitter , 2015, Inf. Syst..