Abstract
Governments are making increasing use of social media technologies, both to inform citizens of available public government services, and to measure the effectiveness of existing services. We describe Vizie, a social media monitoring system designed to help analysts identify how current government services can be improved, by drawing on the commentary and feedback provided in social media by the public using those services. The Vizie system is designed to support the monitoring of arbitrary web and social media content, independent of topical domain and media type. It utilises a variety of natural language processing and information retrieval methods to highlight, distill, and present public feedback. We describe our analysis of the real-world constraints in which the system operates, based on a user requirements analysis which governed our research and development path, including our choice of text analysis methods. The end result is a system that (1) provides an ability to see an overview of the data as well as drill into explore the data in detail, (2) performs text analytics on the social media retrieved and (3) presents contextual information to enable users to decide when to engage with online communities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
http://www.socialmedianews.com.au/social-media-statistics-australia-february-2015/. Accessed on April 26th, 2015.
- 2.
See, for example, the report from PewResearchCenter for the USA http://www.people-press.org/2014/11/13/public-trust-in-government/, or, more generally, http://blog.ted.com/how-pervasive-has-government-distrust-gotten/ ̶̶ Both Accessed August 18th, 2015.
- 3.
- 4.
Application Programming Interface.
- 5.
- 6.
http://www.csiro.au/en/News/News-releases/2015/Enigma-moth-helps-crack-evolutions-code. Accessed August 19th, 2015.
- 7.
- 8.
- 9.
- 10.
- 11.
For example, http://cloud.li/.
- 12.
References
Abel, F., Hauff, C., Houben, G.-J., Stronkman, R., & Ke, T. (2012). Semantics+filtering+search=twitcident. Exploring information in social web streams. In Proceedings of the 23rd ACM Conference on Hypertext and Social Media, Milwaukee, Wisconsin, USA (285—294). ACM, New York, NY.
Barwick, K., Joseph, M., Paris, C., & Wan, S. (2014). Hunters and collectors: Seeking social media content for cultural heritage collection. In Proceedings of the 7th VALA Biennial Conference, Melbourne, Australia.
Bernoff, J. (2008). People don’t trust company blogs. What you should do about it. http://blogs.forrester.com/groundswell/2008/12/people-dont-tru.html. Last viewed August 21, 2015.
Bernstein, M. S., Suh, B., Hong, L., Chen, J., Kairam, S., & Chi, Ed H. (2010). Eddi: interactive topic-based browsing of social status streams. Proceedings of the 23nd annual ACM symposium on User Interface Software and Technology (pp. 303–312). New York, NY, USA: ACM.
Bian, J., Topaloglu, U., & Yu, F. (2012). Towards large-scale Twitter mining for drug-related adverse events. In Proceedings of the 2012 International Workshop on Smart Health and Wellbeing (SHB ’12), New York, NY, USA, 2012.
Bielby, N. (2015). NSW state library wants how-to-vote cards and other election parphenalia. In The Maitland Mercury, March 22 nd , 2015. http://www.maitlandmercury.com.au/story/3350977/word-watch-a-great-apostrophe-epidemic/?cs=179. Accessed September 15th, 2015.
Carbonell, J. G., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. The Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’98) (pp. 335–336). New York, NY, USA: ACM.
Donegan, J. (2015). From Pamphlets to Tweets: Collecting New South Wales Election Material Through the Ages. http://www.abc.net.au/news/2015-03-12/election-tweets-added-to-nsw-library-election-collection/6306490. March 12th, 2015. Accessed September 15th, 2015.
Gooch, D. (2015). Library monitors Twitter Posts as Part of State’s Social History. The Sydney Morning Herald, January 16 th , 2015. Accessed on line September 15th, 2015.
Griffin, G., Jones, R., Paris, C. (2013). Strategic implications of social media for emergency management. In M. Clarke., & G. Griffin (Eds.), Next Generation Disaster and Security Management. Canberra, Australia: Australian Security Research Centre. Publisher: Collaborative Publications. 213-240. ISBN: 978-0-9874332-0-6. 2013.
Government 2.0 Taskforce. (2009). The Government 2.0 Taskforce’s final report: Engage: Getting on with Government 2.0. December 22nd 2009. http://www.finance.gov.au/publications/gov20taskforcereport/index.html. Last viewed April 15th, 2015.
Jacsó, P. (2004). Thoughts about federated searching, information today, 21(9).
Janssen, M., & Estevez, E. (2013). Lean government and platform-based governance—Doing more with less. Government Information Quarterly, 30, S1–S8.
Karimi, S., Wang, C., Alejandro, M. J., Raj, G., Paris, C., & Harvey, B. (2015). Text and data mining techniques in adverse drug reaction detection. ACM Computing Surveys, 47, 2015.
Kulshrestha, J., Zafar, M. B., Noboa, L. E., Hummadi, K. P., & Ghosh, S. (2015). Characterizing information diets of social media users. In The Proceedings of the 2015 International Conference on Web and Social Media (pp. 218–227). UK:Oxford, AAAI.
Leaman, R., Wojtulewicz, L., Sullivan, R., Skariah, A., Yang, J., & Gonzalez, G. (2010). Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks. In Proceedings of the Workshop On Biomedical Natural Language Processing (pp. 117–125), Uppsala, Sweden.
Ladiges, C. (2015). http://csironewsblog.com/2015/03/26/the-election-collection-tracking-trends-on-twitter/ March 26th, 2015. Accessed September 15th, 2015.
Layne, K., & Lee, J. (2001). Developing fully functional E-government: A four stage model. Government Information Quarterly, 18, 122–136.
Linders, D. (2012). From e-government to we-government: Defining a typology for citizen coproduction in the age of social media. Government Information Quarterly, 29, 446–454.
Nielssen. (2009). Global Advertising Consumers Trust Real Friends and Virtual Strangers the Most. Published 07/07/2009. http://www.nielsen.com/us/en/insights/news/2009/global-advertising-consumers-trust-real-friends-and-virtual-strangers-the-most.html. Last viewed April 15th, 2015.
Nikfarjam, A., Sarker, A., O’Connor, K., Ginn, R., & Gonzalez, G. (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. The Oxford University Press.
Nugroho, R., Yang, J., Zhong, Y., Paris, C., & Nepal, S. (2015). Deriving topics in Twitter by exploiting tweet interactions. In The Proceedings of IEEE Big Data Cloud 2015.
Paris, C., Wan, S. (2011). Listening to the community: Social media monitoring tasks for improving government services. In The Proceedings of CHI 2011 Work-In-Progress. April 2011, Vancouver, Canada.
Paul, M., Dredze, M. (2011), You are what you tweet: Analyzing Twitter for public health. In Proceedings of the International AAAI Conference On Weblogs and Social Media (ICWSM) (pp. 265–272), Barcelona, Spain, 2011.
Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on EMNLP. Singapore, August, 248—256. http://www.aclweb.org/anthology/D/D09/D09-1026.
Ramage, D., Dumais, S., & Liebling, D. (2010). Characterizing microblogs with topic models. In Proceedings of the 4 th AAAI International Conference on Weblog and Social Media (ICWSM 2010) (pp. 130–137). Washington D.C., USA: AAAI Press.
Rowlands, T., Thomas, P., & Wan, S. (2009). Web indexing on a diet: template removal with the sandwich algorithm. In The Proceedings of the Australasian Document Computing Symposium (ADCS). Available at http://es.csiro.au/adcs2009/proceedings/poster-presentation/06-rowlands.pdf.
Sadilek, A., Kautz, H. A., & Silenzio, V. (2012). Predicting disease transmission from geo-tagged micro-blog data. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012.
Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: Real-time event detection by social sensors. The Proceedings of the 19th International Conference on World Wide Web (WWW ‘10), Raleigh, North Carolina, USA (pp. 851–860). New York, NY: ACM.
Sankaranarayanan, J., Samet, H., Teitler, B. E, Lieberman, M. D., & Sperling, J. (2009). TwitterStand: News in tweets. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS) (pp. 42–51). Seattle, Washington. ACM, New York, NY, USA. doi:10.1145/1653771.1653781.
Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., & Demirbas, M. (2010). Short text classification in Twitter to improve information filtering. In Proceedings of the 33rd Annual International ACM SIGIR (SIGIR’10) (pp. 841–842). Geneva, Switzerland. ACM, New York, NY, USA. doi:10.1145/1835449.1835643.
Verma, S., Vieweg, S., Corvey, W., Palen, L., Martin, J., Palmer, M., Schram, A., & Anderson, K. (2011). Natural language processing to the rescue? Extracting “situational awareness” tweets during mass emergency. In the Proceedings of the International Conference on Weblogs and Social Media (ICWSM), AAAI Press.
Wan, S., Paris, C., & Dale, R. (2009). Whetting the appetite of scientists: Producing summaries tailored to the citation context. In Proceedings of the 2009 Joint Conference on Digital Libraries (pp. 59–69), Austin, Texas (USA), June 15–19.
Wan, S., & Paris, C. (2014). Improving government services with social media feedback. In Proceedings of the 19th International Conference on Intelligent User Interfaces (IUI 2014) (pp. 27–36). Haifa, Israel, Feb 22–27, 2014.
Wan, S., & Paris, C. (2015). Ranking election issues through the lens of social media. In Proceedings of the 9th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. Beijing, July 2015.
Yin, J., Lampert, A., Cameron, M., Robinson, B., & Power, R. (2012). Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27(6), 52–59. IEEE Computer Society.
Acknowledgments
This research has been partially funded under the Human Services Delivery Research Alliance (HSDRA) between the CSIRO and the Australian Government Department of Human Services, the CSIRO, and the “Early Adopters Group” Programme. We would like to thank P. Aghaei Pour, B. Jin, J. McHugh, A. Gall and H. Asghar for their work on the system, all the communications staff at the Australian Government’s Department of Human Services for their support in this work, and all our other users for their invaluable support and feedback.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Wan, S., Paris, C., Georgakopoulos, D. (2015). Improving Government Services Using Social Media Feedback. In: Nepal, S., Paris, C., Georgakopoulos, D. (eds) Social Media for Government Services. Springer, Cham. https://doi.org/10.1007/978-3-319-27237-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-27237-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27235-1
Online ISBN: 978-3-319-27237-5
eBook Packages: Computer ScienceComputer Science (R0)