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Policy Making Analysis and Practitioner User Experience

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HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies (HCII 2020)

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Abstract

This article presents the work on social media analysis-driven policy-making platforms that are powered by classic social media analysis technologies, such as policy modelling, linguistic analysis, opinion mining, sentiment analysis and information visualization. The approach examines the user design perspective towards user experience in policymaking for all the innovative modules used. The technology behind such complex task is presented while the resulting platform is appraised on the potential for real world application. The findings drive the development and the requirements for the summative usability assessment tests. We also report on the level the practitioners adopted the policy formulation tools.

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References

  1. Capano, G., Pavan, E.: Designing anticipatory policies through the use of ICTs. Policy Soc. 38, 96–117 (2019). https://doi.org/10.1080/14494035.2018.1511194

    Article  Google Scholar 

  2. Spiliotopoulos, D., Dalianis, A., Koryzis, D.: Need driven prototype design for a policy modeling authoring interface. In: Marcus, A. (ed.) DUXU 2014. LNCS, vol. 8518, pp. 481–487. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07626-3_45

    Chapter  Google Scholar 

  3. Knecht, T., Weatherford, M.S.: Public opinion and foreign policy: the stages of presidential decision making. Int. Stud. Q. 50, 705–727 (2006). https://doi.org/10.1111/j.1468-2478.2006.00421.x

    Article  Google Scholar 

  4. Jasti, S., Mahalakshmi, T.S.: A review on sentiment analysis of opinion mining. In: Mallick, P.K., Balas, V.E., Bhoi, A.K., Zobaa, A.F. (eds.) Cognitive Informatics and Soft Computing. AISC, vol. 768, pp. 603–612. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0617-4_58

    Chapter  Google Scholar 

  5. Murray, G., Hoque, E., Carenini, G.: Opinion summarization and visualization. In: Sentiment Analysis in Social Networks, pp. 171–187. Elsevier (2017). https://doi.org/10.1016/B978-0-12-804412-4.00011-5

  6. Liu, B.: Sentiment Analysis and Opinion Mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012). https://doi.org/10.2200/S00416ED1V01Y201204HLT016

    Article  Google Scholar 

  7. Hardina, D.: Strategies for citizen participation and empowerment in non-profit community-based organizations. Community Dev. 37, 4–17 (2006). https://doi.org/10.1080/15575330609490192

    Article  Google Scholar 

  8. Braga, D.D.S., Niemann, M., Hellingrath, B., Neto, F.B.D.L.: Survey on computational trust and reputation models. ACM Comput. Surv. 51, 1–40 (2019). https://doi.org/10.1145/3236008

    Article  Google Scholar 

  9. Tambouris, E., et al.: eParticipation in Europe. In: E-Government Success around the World: Cases, Empirical Studies, and Practical Recommendations, pp. 341–357 (2013). https://doi.org/10.4018/978-1-4666-4173-0.ch017

  10. Alexopoulos, C., Lachana, Z., Androutsopoulou, A., Diamantopoulou, V., Charalabidis, Y., Loutsaris, M.A.: How machine learning is changing e-government. In: Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance - ICEGOV2019, pp. 354–363. ACM Press, New York (2019). https://doi.org/10.1145/3326365.3326412

  11. Rowledge, L.R.: CrowdRising: Building a Sustainable World through Mass Collaboration. Routledge, Abingdon (2019). https://doi.org/10.4324/9780429285905

  12. Schefbeck, G., Spiliotopoulos, D., Risse, T.: The recent challenge in web archiving: archiving the social web. In: Proceedings of the International Council on Archives Congress, pp. 1–5 (2012)

    Google Scholar 

  13. Fitsilis, F., Koryzis, D., Svolopoulos, V., Spiliotopoulos, D.: Implementing digital parliament innovative concepts for citizens and policy makers. In: Nah, F.F.-H., Tan, C.-H. (eds.) HCIBGO 2017. LNCS, vol. 10293, pp. 154–170. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58481-2_13

    Chapter  Google Scholar 

  14. Howlett, M., Cashore, B.: Conceptualizing public policy. In: Engeli, I., Allison, C.R. (eds.) Comparative Policy Studies. RMS, pp. 17–33. Palgrave Macmillan UK, London (2014). https://doi.org/10.1057/9781137314154_2

    Chapter  Google Scholar 

  15. Sartor, G.: Legislative information and the web. In: Legislative XML for the Semantic Web, pp. 11–20. Springer, Dordrecht (2011). https://doi.org/10.1007/978-94-007-1887-6_2

  16. Kouroupetroglou, G., Spiliotopoulos, D.: Usability methodologies for real-life voice user interfaces. Int. J. Inf. Technol. Web. Eng. 4, 78–94 (2009). https://doi.org/10.4018/jitwe.2009100105

    Article  Google Scholar 

  17. Hossain, M.A., Dwivedi, Y.K., Rana, N.P.: State-of-the-art in open data research: Insights from existing literature and a research agenda. J. Organ. Comput. Electron. Commer. 26, 14–40 (2016). https://doi.org/10.1080/10919392.2015.1124007

    Article  Google Scholar 

  18. Margaris, D., Georgiadis, P., Vassilakis, C.: On replacement service selection in WS-BPEL scenario adaptation. In: Proceedings - 2015 IEEE 8th International Conference on Service-Oriented Computing and Applications, SOCA 2015, pp. 10–17 (2015). https://doi.org/10.1109/SOCA.2015.11

  19. Margaris, D., Vassilakis, C., Georgiadis, P.: Improving QoS delivered by WS-BPEL scenario adaptation through service execution parallelization. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 1590–1596. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2851613.2851805

  20. Margaris, D., Georgiadis, P., Vassilakis, C.: A collaborative filtering algorithm with clustering for personalized web service selection in business processes. In: 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS), pp. 169–180 (2015). https://doi.org/10.1109/RCIS.2015.7128877

  21. Spiliotopoulos, D., Xydas, G., Kouroupetroglou, G.: diction based prosody modeling in table-to-speech synthesis. In: Matoušek, V., Mautner, P., Pavelka, T. (eds.) TSD 2005. LNCS (LNAI), vol. 3658, pp. 294–301. Springer, Heidelberg (2005). https://doi.org/10.1007/11551874_38

    Chapter  Google Scholar 

  22. Risse, T., et al.: The ARCOMEM architecture for social- and semantic-driven web archiving. Future Internet 6, 688–716 (2014). https://doi.org/10.3390/fi6040688

    Article  Google Scholar 

  23. Margaris, D., Vassilakis, C., Georgiadis, P.: An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques. Sci. Comput. Program. 98, 707–734 (2015). https://doi.org/10.1016/j.scico.2014.10.007

    Article  Google Scholar 

  24. Margaris, D., Georgiadis, P., Vassilakis, C.: Adapting WS-BPEL scenario execution using collaborative filtering techniques. In: Proceedings - International Conference on Research Challenges in Information Science, pp. 174–184 (2013). https://doi.org/10.1109/RCIS.2013.6577691

  25. Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., Mora, H.: Managing marketing decision-making with sentiment analysis: an evaluation of the main product features using text data mining. Sustainability 11, 4235 (2019). https://doi.org/10.3390/su11154235

    Article  Google Scholar 

  26. Margaris, D., Vassilakis, C., Spiliotopoulos, D.: What makes a review a reliable rating in recommender systems? Inf. Process. Manage. 57, 102304 (2020). https://doi.org/10.1016/j.ipm.2020.102304

    Article  Google Scholar 

  27. Margaris, D., Vassilakis, C., Spiliotopoulos, D.: Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks. Soc. Netw. Anal. Mining 9(1), 1–19 (2019). https://doi.org/10.1007/s13278-019-0610-x

    Article  Google Scholar 

  28. Pino, A., Kouroupetroglou, G., Kacorri, H., Sarantidou, A., Spiliotopoulos, D.: An open source/freeware assistive technology software inventory. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds.) ICCHP 2010. LNCS, vol. 6179, pp. 178–185. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14097-6_29

    Chapter  Google Scholar 

  29. Margaris, D., Vassilakis, C.: Exploiting Internet of Things information to enhance venues’ recommendation accuracy. Serv. Oriented Comput. Appl. 11(4), 393–409 (2017). https://doi.org/10.1007/s11761-017-0216-y

    Article  Google Scholar 

  30. Margaris, D., Spiliotopoulos, D., Vassilakis, C.: Social relations versus near neighbours: reliable recommenders in limited information social network collaborative filtering for online advertising. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), pp. 1160–1167. ACM, Vancouver (2019). https://doi.org/10.1145/3341161.3345620

  31. Xydas, G., Spiliotopoulos, D., Kouroupetroglou, G.: Modeling improved prosody generation from high-level linguistically annotated corpora. IEICE Trans. Inf. Syst. E88-D, 510–518 (2005). https://doi.org/10.1093/ietisy/e88-d.3.510

  32. Spiliotopoulos, D., Stavropoulou, P., Kouroupetroglou, G.: Acoustic rendering of data tables using earcons and prosody for document accessibility. In: Stephanidis, C. (ed.) UAHCI 2009. LNCS, vol. 5616, pp. 587–596. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02713-0_62

    Chapter  Google Scholar 

  33. Mallan, K.: Gateways to digital participation. In: Digital Participation through Social Living Labs, pp. 333–349. Elsevier (2018). https://doi.org/10.1016/B978-0-08-102059-3.00018-6

  34. Demidova, E., et al.: Analysing and enriching focused semantic web archives for parliament applications. Future Internet 6, 433–456 (2014). https://doi.org/10.3390/fi6030433

    Article  Google Scholar 

  35. Androutsopoulos, I., Spiliotopoulos, D., Stamatakis, K., Dimitromanolaki, A., Karkaletsis, V., Spyropoulos, C.D.: Symbolic authoring for multilingual natural language generation. In: Vlahavas, I.P., Spyropoulos, C.D. (eds.) SETN 2002. LNCS (LNAI), vol. 2308, pp. 131–142. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46014-4_13

    Chapter  Google Scholar 

  36. Antonakaki, D., Spiliotopoulos, D., Samaras, C.V., Ioannidis, S., Fragopoulou, P.: Investigating the complete corpus of referendum and elections tweets. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, pp. 100–105 (2016). https://doi.org/10.1109/ASONAM.2016.7752220

  37. Margaris, D., Vassilakis, C., Georgiadis, P.: Knowledge-based leisure time recommendations in social networks. In: Alor-Hernández, G., Valencia-García, R. (eds.) Current Trends on Knowledge-Based Systems. ISRL, vol. 120, pp. 23–48. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51905-0_2

    Chapter  Google Scholar 

  38. Margaris, D., Vassilakis, C., Georgiadis, P.: Recommendation information diffusion in social networks considering user influence and semantics. Soc. Netw. Anal. Mining 6(1), 1–22 (2016). https://doi.org/10.1007/s13278-016-0416-z

    Article  Google Scholar 

  39. Eckardt, M.: The Impact of ICT on policies, politics, and polities an evolutionary economics approach to information and communication technologies (ICT). SSRN Electron. J. 20 (2012). https://doi.org/10.2139/ssrn.2445839

  40. Margaris, D., Vassilakis, C.: Exploiting rating abstention intervals for addressing concept drift in social network recommender systems. Informatics. 5, 21 (2018). https://doi.org/10.3390/informatics5020021

    Article  Google Scholar 

  41. Aivazoglou, M., et al.: A fine-grained social network recommender system. Soc. Netw. Anal. Mining 10(1), 1–18 (2019). https://doi.org/10.1007/s13278-019-0621-7

    Article  Google Scholar 

  42. Norton, P.: Post-legislative scrutiny in the UK Parliament: adding value. J. Legis. Stud. 25, 340–357 (2019). https://doi.org/10.1080/13572334.2019.1633778

    Article  Google Scholar 

  43. Griffith, J., Leston-Bandeira, C.: How are parliaments using new media to engage with citizens? J. Legis. Stud. 18, 496–513 (2012). https://doi.org/10.1080/13572334.2012.706058

    Article  Google Scholar 

  44. Makri, E., Spiliotopoulos, D., Vassilakis, C., Margaris, D.: Human behaviour in multimodal interaction: main effects of civic action and interpersonal and problem-solving skills. J. Ambient Intell. Hum. Comput. 1, 1–16 (2020). https://doi.org/10.1007/s12652-020-01846-x

    Article  Google Scholar 

  45. Margaris, D., Vassilakis, C., Georgiadis, P.: Query personalization using social network information and collaborative filtering techniques. Future Gener. Comput. Syst. 78, 440–450 (2018). https://doi.org/10.1016/j.future.2017.03.015

    Article  Google Scholar 

  46. Margaris, D., Kobusinska, A., Spiliotopoulos, D., Vassilakis, C.: An adaptive social network-aware collaborative filtering algorithm for improved rating prediction accuracy. IEEE Access. 8, 68301–68310 (2020). https://doi.org/10.1109/ACCESS.2020.2981567

    Article  Google Scholar 

  47. Margaris, D., Vassilakis, C.: Improving collaborative filtering’s rating prediction quality in dense datasets, by pruning old ratings. In: Proceedings - IEEE Symposium on Computers and Communications, pp. 1168–1174 (2017). https://doi.org/10.1109/ISCC.2017.8024683

  48. Margaris, D., Vassilakis, C.: Improving collaborative filtering’s rating prediction accuracy by considering users’ rating variability. In: Proceedings of the 2018 IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, 16th International Conference on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, pp. 1022–1027 (2018). https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00145

  49. Margaris, D., Vasilopoulos, D., Vassilakis, C., Spiliotopoulos, D.: Improving collaborative filtering’s rating prediction accuracy by introducing the common item rating past criterion. In: 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019, pp. 1022–1027 (2019). https://doi.org/10.1109/IISA.2019.8900758

  50. Margaris, D., Vassilakis, C.: Improving collaborative filtering’s rating prediction quality by considering shifts in rating practices. In: 2017 IEEE 19th Conference on Business Informatics (CBI), pp. 158–166 (2017). https://doi.org/10.1109/CBI.2017.24

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Author information

Authors and Affiliations

  1. Hellenic Parliament, Athens, Greece

    Dimitris Koryzis & Fotios Fitsilis

  2. Department of Informatics and Telecommunications, University of the Peloponnese, Tripoli, Greece

    Dimitris Spiliotopoulos & Costas Vassilakis

  3. Department of Cultural Technology and Communication, University of the Aegean, Lesvos, Greece

    Theocharis Theocharopoulos

  4. Department of Informatics and Telecommunications, University of Athens, Athens, Greece

    Dionisis Margaris

Authors
  1. Dimitris Koryzis
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  2. Fotios Fitsilis
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  3. Dimitris Spiliotopoulos
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  4. Theocharis Theocharopoulos
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  5. Dionisis Margaris
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  6. Costas Vassilakis
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Corresponding author

Correspondence to Dimitris Spiliotopoulos .

Editor information

Editors and Affiliations

  1. University of Crete and Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

  3. World Usability Day and Bentley User Experience Center, Newton Center, MA, USA

    Elizabeth Rosenzweig

  4. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

  5. San Jose State University, San Jose, CA, USA

    Abbas Moallem

  6. Eindhoven Univesity of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Matthias Rauterberg

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Koryzis, D., Fitsilis, F., Spiliotopoulos, D., Theocharopoulos, T., Margaris, D., Vassilakis, C. (2020). Policy Making Analysis and Practitioner User Experience. In: Stephanidis, C., Marcus, A., Rosenzweig, E., Rau, PL.P., Moallem, A., Rauterberg, M. (eds) HCI International 2020 - Late Breaking Papers: User Experience Design and Case Studies. HCII 2020. Lecture Notes in Computer Science(), vol 12423. Springer, Cham. https://doi.org/10.1007/978-3-030-60114-0_29

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  • DOI: https://doi.org/10.1007/978-3-030-60114-0_29

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