4,868
Views
25
CrossRef citations to date
0
Altmetric
Special Section: Social Media and Tracking Data

A conceptual framework for studying collective reactions to events in location-based social media

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon &
Pages 780-804 | Received 09 Aug 2017, Accepted 06 Nov 2018, Published online: 18 Nov 2018

References

  • Allen, E., Edwards, G., and Beard, Y., 1995. Qualitative causal modeling in temporal GIS. Lecture Notes in Computer Science. No. 988, 397 (988), 397.
  • Allen, J.F. and Hayes, P.J., 1989. Moments and points in an interval‐based temporal logic. Computational Intelligence, 5 (3), 225–238. doi:10.1111/j.1467-8640.1989.tb00329.x
  • Amanatullah, B., et al., 2013. Temporally aligning clusters of social media reaction to speech events. Proceedings of The 2013 World Congress in Computer Science, Computer Engineering, and Applied Computing, 22–25 July Las Vegas.
  • Andrienko, N., et al., 2015. Detection, tracking, and visualization of spatial event clusters for real time monitoring. IEEE/ACM Data Science and Advanced Analytics (DSAA), Paris, 1–10. doi:10.1109/DSAA.2015.7344880
  • Beard, K., Deese, H., and Pettigrew, N.R., 2008. A framework for visualization and exploration of events. Information Visualization, 7, 133–151. doi:10.1057/palgrave.ivs.9500165
  • Bell, S., 2012. Landscape: Pattern, perception and process. Abingdon: Routledge.
  • Brabham, D.C., 2013. Crowdsourcing. MIT Press. Available from: http://www.jstor.org/stable/j.ctt5hhk3m [Accessed 9 August 2017].
  • Burnap, P., et al., 2014. Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack. Social Network Analysis and Mining, 4, 1–14. doi:10.1007/s13278-014-0206-4
  • Castillo, C., et al., 2014. Characterizing the life cycle of online news stories using social media reactions. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, 14–18 March Vancouver. 211–223. doi:10.1145/2531602.2531623
  • Chen, X., 2003. Object and event concepts: A cognitive mechanism of incommensurability. Philosophy Of Science, 70, 962–974. doi:10.1086/377381
  • Chen, Y., Parkins, R., and Sherren, K., 2018. Using geo-tagged Instagram posts to reveal landscape values around current and proposed hydroelectric dams and their reservoirs. In: Landscape and Urban Planning, vol. 170. Elsevier, 283–292. doi:10.1016/j.landurbplan.2017.07.004.
  • Claramunt, C. and Jiang, B., 2000. Hierarchical reasoning in time and space. Proceedings of 9th International Symposium on Spatial Data Handling, Beijing, 1–10. Available from http://fromto.hig.se/~bjg/Clara%26JiangSDH.PDF [Accessed 9 August 2017].
  • Crane, R. and Sornette, D., 2008. Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences, 105, 15649–15653. doi:10.1073/pnas.0803685105
  • Davidson, D., 1980. Essays on actions and events. Journal of Philosophy, 1, 0–19. doi:10.1093/0199246270.001.0001
  • De, C.M., et al., 2010. How does the data sampling strategy impact the discovery of information diffusion in social media? Proceedings of the AAAI Conference on Weblogs and Social Media, 23 – 26 May 2010 Washington, CA: AAAI Press, 34–41.
  • Dearden, L., 2016. Anger over “Bregret” as Leave voters say they thought UK would stay in EU. The Independent. Available from http://www.independent.co.uk/news/uk/politics/brexit-anger-bregret-leave-voters-protest-vote-thought-uk-stay-in-eu-remain-win-a7102516.html [Accessed 9 August 2017].
  • Downs, A., 1972. Up and down with ecology─the issue-attention cycle. Public Interest, 28, 38.
  • Dustdar, S. and Rosenberg, F., 2007. A survey on context-aware systems. Information Systems, p. 2.
  • Egenhofer, M.J. and Franzosa, R.D., 1991. Point-set topological spatial relations. International Journal of Geographical Information Systems, 5 (2), 161–174. doi:10.1080/02693799108927841
  • Etzion, O. and Niblett, P., 2010. Event processing in action [online]. Available from http://dl.acm.org/citation.cfm?id=1894960 [Accessed 9 August 2017].
  • Fung, I.C.H., et al., 2015. Chinese social media reaction to information about 42 notifiable infectious diseases. PLOS ONE, 10, 1–16. doi:10.1371/journal.pone.0126092
  • Galton, A., 2006. On what goes on: the ontology of processes and events. Formal Ontology in Information Systems. IOS Press, 4. Available from http://www.comp.leeds.ac.uk/brandon/FOIS-06/CRC/Part-0-InvitedTalks/02_fois06.pdf [Accessed 9 August 2017].
  • Galton, A., 2008. Experience and history: processes and their relation to events. Journal of Logic and Computation, 18, 323–340. doi:10.1093/logcom/exm079
  • Galton, A., 2015. Outline of a formal theory of processes and events, and why GIScience needs one. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9368, 3–22. doi:10.1007/978-3-319-23374-1_1
  • Galton, A. and Mizoguchi, R., 2009. The water falls but the waterfall does not fall: new perspectives on objects, processes and events. Applied Ontology, 4, 71–107. doi:10.3233/AO-2009-0067
  • Gao, H., et al., 2014. Modeling user attitude toward controversial topics in online social media. Eighth International AAAI Conference on Weblogs and Social Media, 27–29 May 2015 Oxford, 121–130. Available from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewPDFInterstitial/8058/8112 [Accessed 9 August 2017].
  • Hahmann, S., Purves, R., and Burghardt, D., 2014. Twitter location (sometimes) matters: exploring the relationship between georeferenced tweet content and nearby feature classes. Journal of Spatial Information Science, 9, 1–36. doi:10.5311/JOSIS.2014.9.185
  • Hashimoto, T., et al., 2013. Comparison of reaction in social media after the East Japan Great Earthquake between Thailand and Japan. 13th International Symposium on Communications and Information Technologies: Communication and Information Technology for New Life Style Beyond the Cloud, ISCIT 2013, Surat Thani, Thailand, 781–786. doi:10.1109/ISCIT.2013.6645967
  • Hauthal, E., 2013. Detection, modelling and visualisation of georeferenced emotions from user-generated content. 26th International Cartographic Conference Proceedings, 25–30 August 2013, Dresden.
  • He, J., et al., 2015. Uncovering social media reaction pattern to protest events: A spatiotemporal dynamics perspective of Ferguson unrest. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9471, 67–81. doi:10.1007/978-3-319-27433-1_5
  • Hecht, B., et al., 2011. Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. Proceedings of the SIGCHI conference on human factors in computing systems,Vancouver, ACM, 237–246. doi:10.1145/1978942.1978976
  • Hecht, B. and Gergle, D., 2010. On the “Localness” of user-generated content. In: Proceedings of the ACM conference on Computer supported cooperative work, New York, pp. 229–232.
  • Hickey, K.R., 2014. A review of the 2013 hurricane, tropical cyclone and typhoon season. International Journal of Meteorology,UK, 39 (386), 154–159.
  • Kounadi, O., et al., 2015. Exploring twitter to analyze the public’s reaction patterns to recently reported homicides in London. PLOS ONE, 10, 1–17. doi:10.1371/journal.pone.0121848
  • Kuhn, W., 2012. Core concepts of spatial information for transdisciplinary research. International Journal of Geographical Information Science, 26, 2267–2276. doi:10.1080/13658816.2012.722637
  • Li, J., et al., 2018. Visual exploration of spatial and temporal variations of tweet topic popularity. In: C. Tominski and T. von Landesberger, eds. EuroVis workshop on Visual Analytics (EuroVA). Brno, Czech Republic: The Eurographics Association, 7–11. doi:10.2312/eurova.20181105
  • Lipizzi, C., Iandoli, L., and Marquez, J.E.R., 2016. Combining structure, content and meaning in online social networks: the analysis of public’s early reaction in social media to newly launched movies. Technological Forecasting and Social Change, 109, 35–49. doi:10.1016/j.techfore.2016.05.013
  • Liu, Y., et al., 2008. Towards a spatiotemporal event-oriented ontology. Artificial Intelligence, 18–20 October 2015.
  • Lyons, J., 1977. Semantics: volume 2. Cambridge: Cambridge University Press, 1, 388.
  • McEnery, T., McGlashan, M., and Love, R., 2015. Press and social media reaction to ideologically inspired murder: the case of Lee Rigby. Discourse & Communication, 9, 237–259. doi:10.1177/1750481314568545
  • Meaney, S., et al., 2016. Reaction on twitter to a cluster of perinatal deaths: a mixed method study. JMIR Public Health and Surveillance, p. 2. doi:10.2196/publichealth.5333
  • Mitrou, L., et al., 2014. Social media profiling: A Panopticon or Omniopticon tool? Proceedings of the 6th Conference of the Surveillance Studies Network,Barcelona, Spain, 1–15.
  • Nikfarjam, A., et al., 2015. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22, 671–681. doi:10.1093/jamia/ocu041
  • Nov, O., Naaman, M., and Ye, C., 2009. Analysis of participation in an online photo-sharing community: a multidimensional perspective. Journal of the American Society for Information Science and Technology. 14. doi:10.1002/asi.21278
  • Polous, K., Krisp, J., and Meng, L., 2012. The note on event for event detection from Volunteered Geographic Information (VGI). Ohio: GIScience.
  • Quine, W.V.O., 1985. Events and reification. Actions and Events: Perspectives on the Philosophy of Donald Davidson, 162–171. Available from http://www.fitelson.org/125/quine_events.pdf [Accessed 9 August 2017].
  • Robertson, C. and Horrocks, K., 2017. Spatial Context from Open and Online Processing (SCOOP): geographic, temporal, and thematic analysis of online information sources. ISPRS International Journal of Geo-Information, 6, 193. doi:10.3390/ijgi6070193
  • Rodrigues, R.A., 2016. #Femvertising : empowering women through the hashtag? A comparative analysis of consumers’ reaction to feminist advertising on twitter. Thesis. Lisbon School of Economics & Management.
  • Saito, S., et al., 2015. Tracking time evolution of collective attention clusters in twitter: time evolving nonnegative matrix factorisation. PLOS ONE, 10, 1–17. doi:10.1371/journal.pone.0139085
  • Shatford, S., 1986. Analyzing the subject of a picture: a theoretical approach. Cataloging & Classification Quarterly, 6, 39–62. doi:10.1300/J104v06n03_04
  • Sievert, C. and Shirley, K., 2014. LDAvis: a method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, Baltimore, 63–70. doi:10.3115/v1/W14-3110
  • Sui, D. and Goodchild, M., 2011. The convergence of GIS and social media: challenges for GIScience. International Journal of Geographical Information Science, 25 (11), 1737–1748. doi:10.1080/13658816.2011.604636
  • Szomszor, M., Kostkova, P., and St Louis, C., 2011. Twitter informatics: tracking and understanding public reaction during the 2009 Swine Flu pandemic. Proceedings of the ACM International Conference on Web Intelligence, WI 1 August 2011 Lyon, 320–323. doi:10.1109/WI-IAT.2011.311
  • Teitler, B.E., et al., 2008. NewsStand: A new view on news. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, 5–7 November 2008 Irvine, CA. NY: ACM, 18.
  • Towne, W., Rosé, C., and Herbsleb, J., 2016. Measuring similarity similarly: LDA and human perception. ACM transactions on intelligent systems and technology ACM reference format ACM trans. Intell. Syst. Technol, 7(2), 25, 1–25:29. doi:10.1145/0000000.0000000.
  • Tsytsarau, M., Palpanas, T., and Castellanos, M., 2014. Dynamics of news events and social media reaction. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 24–27 August 2014 NY. ACM, 901–910. doi:10.1145/2623330.2623670
  • Westermann, U. and Jain, R., 2007. Toward a common event model for multimedia applications. IEEE Multimedia, 14, 19–29. doi:10.1109/MMUL.2007.23
  • Worboys, M., 2005. Event‐oriented approaches to geographic phenomena. International Journal of Geographical Information Science, 19, 1–28. doi:10.1080/13658810412331280167
  • Worboys, M. and Hornsby, K., 2004. From objects to events: GEM, the geospatial event model. GIScience, 3234, 1–17. doi:10.1007/978-3-540-30231-5_22
  • You, A., et al., 2012. Detecting automation of twitter accounts: are you a human, bot, or cyborg? IEEE Transactions on Dependable and Secure Computing, 9, 811–824. doi:10.1109/TDSC.2012.75
  • Zacks, J.M., et al., 2007. Event perception: A mind-brain perspective - ProQuest. Psychological Bulletin, 133, 273–293. doi:10.1037/0033-2909.133.2.273
  • Zacks, J.M. and Tversky, B., 2001. Event structure in perception and conception. Psychological Bulletin, 127, 3–21. doi:10.1037/0033-2909.127.1.3
  • Zeng, L., Starbird, K., and Spiro, E.S., 2016. #Unconfirmed: classifying rumor stance in crisis-related social media messages. Proceedings of the International AAAI Conference on Weblogs and Social Media, 17–20 May 2016, Maternushaus, Germany: AAAI Press. 747–750. Available from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/viewFile/13075/12845 [Accessed 9 August 2017].
  • Zimmermann, A., Lorenz, A., and Oppermann, R., 2007. An operational definition of context. Proceedings: 6th International and Interdisciplinary Conference, CONTEXT 20 – 24 August 2007, Denmark: Springer, 558–571.