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A Systematic Review of Social Media for Intelligent Human-Computer Interaction Research: Why Smart Social Media is Not Enough

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Intelligent Human Computer Interaction (IHCI 2020)

Abstract

As social media shapes human behavior and social interactions, especially with the help of Big Data and artificial intelligence, it becomes an important site for policy and design interventions. Since no systematic review on social media research for intelligent HCI has been conducted, the article presents exploratory findings on a scientometric analysis of the literature at the intersections of social media and AI. By identifying and discussing the main and emerging disciplines and the related keywords from 2,443 articles along with more than 18,000 citations, the findings show that while Twitter and Facebook have been the main platforms for study, Chinese social media platforms emerge as new sites of research with the COVID-19. Also, sentiment analysis appears to be the most prominent research practices, with implications on the issues of privacy, misinformation, depression, and mental health). Four key dimensions of social media are summarized as foundations for the proposed research agenda for intelligent HCI that is not only smart, but also fair and inclusive.

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References

  1. Griffiths, M.D., Kuss, D.J., Demetrovics, Z.: Social networking addiction. In: Behavioral Addictions, pp. 119–141. Elsevier (2014). https://doi.org/10.1016/B978-0-12-407724-9.00006-9

  2. United Nations General Assembly: Road map for digital cooperation: implementation of the recommendations of the High-level Panel on Digital Cooperation (2020)

    Google Scholar 

  3. Chui, M., Harrysson, M., Manyika, J., Roberts, R.: Applying AI for Social Good. Mckinsey Global Institute (2018)

    Google Scholar 

  4. Garcia, C.: A nearest-neighbor algorithm for targeted interaction design in social outreach campaigns. Kybernetes 45, 1243–1256 (2016). https://doi.org/10.1108/K-09-2015-0236

    Article  Google Scholar 

  5. Maguire, M.: Socio-technical systems and interaction design – 21st century relevance. Appl. Ergon. 45, 162–170 (2014). https://doi.org/10.1016/j.apergo.2013.05.011

    Article  Google Scholar 

  6. Blandford, A.: Intelligent interaction design: the role of human-computer interaction research in the design of intelligent systems. Expert Syst. 18, 3–18 (2001). https://doi.org/10.1111/1468-0394.00151

    Article  Google Scholar 

  7. Anderson, J., Rainie, L., Luchsinger, A.: Artificial Intelligence and the Future of Humans. Pew Research Center (2018)

    Google Scholar 

  8. Garfield, E.: Research fronts. Current Comments (1994)

    Google Scholar 

  9. Clarivate Analytics: Research Areas (Categories/Classification). https://images.webofknowledge.com/WOKRS535R100/help/WOS/hp_research_areas_easca.html. Accessed 01 Nov 2020

  10. Clarivate Analytics: Web of Science categories. https://images.webofknowledge.com/WOKRS535R100/help/WOS/hp_subject_category_terms_tasca.html. Accessed 01 Nov 2020

  11. Rath, B., Gao, W., Ma, J., Srivastava, J.: Utilizing computational trust to identify rumor spreaders on Twitter. Soc. Netw. Anal. Min. 8(1), 1–16 (2018). https://doi.org/10.1007/s13278-018-0540-z

    Article  Google Scholar 

  12. Burdisso, S.G., Errecalde, M., Montes-y-Gómez, M.: A text classification framework for simple and effective early depression detection over social media streams. Expert Syst. Appl. 133, 182–197 (2019). https://doi.org/10.1016/j.eswa.2019.05.023

    Article  Google Scholar 

  13. Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40, 6266–6282 (2013). https://doi.org/10.1016/j.eswa.2013.05.057

    Article  Google Scholar 

  14. Ortigosa, A., Martín, J.M., Carro, R.M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014). https://doi.org/10.1016/j.chb.2013.05.024

    Article  Google Scholar 

  15. Poria, S., Cambria, E., Winterstein, G., Huang, G.-B.: Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl.-Based Syst. 69, 45–63 (2014). https://doi.org/10.1016/j.knosys.2014.05.005

    Article  Google Scholar 

  16. Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl.-Based Syst. 89, 14–46 (2015). https://doi.org/10.1016/j.knosys.2015.06.015

    Article  Google Scholar 

  17. Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. 63, 163–173 (2012). https://doi.org/10.1002/asi.21662

    Article  Google Scholar 

  18. Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. 110, 5802–5805 (2013). https://doi.org/10.1073/pnas.1218772110

    Article  Google Scholar 

  19. Kimura, A., Duh, K., Hirao, T., Ishiguro, K., Iwata, T., Au Yeung, A.: Creating stories from socially curated microblog messages. IEICE Trans. Inf. Syst. E97.D, 1557–1566 (2014). https://doi.org/10.1587/transinf.E97.D.1557

  20. Stella, M., Ferrara, E., De Domenico, M.: Bots increase exposure to negative and inflammatory content in online social systems. Proc. Natl. Acad. Sci. USA 115, 12435–12440 (2018). https://doi.org/10.1073/pnas.1803470115

    Article  Google Scholar 

  21. Lee, S.: Detection of political manipulation in online communities through measures of effort and collaboration. ACM Trans. Web 9, 1–24 (2015). https://doi.org/10.1145/2767134

    Article  Google Scholar 

  22. Liu, H., Maes, P., Davenport, G.: Unraveling the taste fabric of social networks. Int. J. Semant. Web Inf. Syst. 2, 42–71 (2006). https://doi.org/10.4018/jswis.2006010102

    Article  Google Scholar 

  23. Sandoval Orozco, A.L., Quinto Huamán, C., Povedano Álvarez, D., García Villalba, L.J.: A machine learning forensics technique to detect post-processing in digital videos. Future Gener. Comput. Syst. 111, 199–212 (2020). https://doi.org/10.1016/j.future.2020.04.041

    Article  Google Scholar 

  24. Nguyen, T., Phung, D., Dao, B., Venkatesh, S., Berk, M.: Affective and content analysis of online depression communities. IEEE Trans. Affect. Comput. 5, 217–226 (2014). https://doi.org/10.1109/TAFFC.2014.2315623

    Article  Google Scholar 

  25. Prieto, V.M., Matos, S., Álvarez, M., Cacheda, F., Oliveira, J.L.: Twitter: a good place to detect health conditions. PLoS ONE 9, e86191 (2014). https://doi.org/10.1371/journal.pone.0086191

    Article  Google Scholar 

  26. Reece, A.G., Danforth, C.M.: Instagram photos reveal predictive markers of depression. EPJ Data Sci. 6, 15 (2017). https://doi.org/10.1140/epjds/s13688-017-0110-z

    Article  Google Scholar 

  27. Cheng, Q., Li, T.M., Kwok, C.-L., Zhu, T., Yip, P.S.: Assessing suicide risk and emotional distress in chinese social media: a text mining and machine learning study. J. Med. Internet Res. 19, e243 (2017). https://doi.org/10.2196/jmir.7276

    Article  Google Scholar 

  28. Li, S., Wang, Y., Xue, J., Zhao, N., Zhu, T.: The impact of COVID-19 epidemic declaration on psychological consequences: a study on active weibo users. IJERPH 17, 2032 (2020). https://doi.org/10.3390/ijerph17062032

    Article  Google Scholar 

  29. Wu, W., et al.: Psychological stress of medical staffs during outbreak of COVID-19 and adjustment strategy. J. Med. Virol. 92, 1962–1970 (2020). https://doi.org/10.1002/jmv.25914

    Article  Google Scholar 

  30. Gupta, L., Gasparyan, A.Y., Misra, D.P., Agarwal, V., Zimba, O., Yessirkepov, M.: Information and misinformation on COVID-19: a cross-sectional survey study. J. Korean Med. Sci. 35, e256 (2020). https://doi.org/10.3346/jkms.2020.35.e256

    Article  Google Scholar 

  31. Ye, Q., Zhou, J., Wu, H.: Using information technology to manage the COVID-19 pandemic: development of a technical framework based on practical experience in China. JMIR Med. Inform. 8, e19515 (2020). https://doi.org/10.2196/19515

    Article  Google Scholar 

  32. Poom, A., Järv, O., Zook, M., Toivonen, T.: COVID-19 is spatial: ensuring that mobile Big Data is used for social good. Big Data Soc. 7, 205395172095208 (2020). https://doi.org/10.1177/2053951720952088

  33. Zhenghong, P., Wang, R., Liu, L., Wu, H.: Exploring urban spatial features of COVID-19 transmission in wuhan based on social media data. ISPRS Int. J. Geo-Inf. 9, 402 (2020). https://doi.org/10.3390/ijgi9060402

    Article  Google Scholar 

  34. Shen, C., Chen, A., Luo, C., Zhang, J., Feng, B., Liao, W.: Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in mainland china: observational infoveillance study. J. Med. Internet Res. 22, e19421 (2020). https://doi.org/10.2196/19421

    Article  Google Scholar 

  35. Hua, J., Shaw, R.: Corona Virus (COVID-19) “Infodemic” and emerging issues through a data lens: the case of China. Int. J. Environ. Res. Public Health 17, 2309 (2020). https://doi.org/10.3390/ijerph17072309

  36. Mühlhoff, R.: Human-aided artificial intelligence: or, how to run large computations in human brains? Toward a media sociology of machine learning. New Media Soc. 22, 1868–1884 (2020). https://doi.org/10.1177/1461444819885334

    Article  Google Scholar 

  37. Bühring, J., Patricia, A.M., Torkkeli, M., de Engenharia, F.: Emotional and social intelligence as ‘Magic Key’ in innovation: a designer’s call toward inclusivity for all. J. Innov. Manag 6 (2018)

    Google Scholar 

  38. Steinfeld, N., Lev-On, A.: Top-down, non-inclusive and non-egalitarian: characterizing the communication of members of parliament with the public on their Facebook pages. Presented at the June 18 (2019). https://doi.org/10.1145/3325112.3325249

  39. Pak, B., Chua, A., Vande Moere, A.: FixMyStreet brussels: socio-demographic inequality in crowdsourced civic participation. J. Urban Technol. 24.0, 65 (2017). https://doi.org/10.1080/10630732.2016.1270047

  40. Haworth, B., Bruce, E., Whittaker, J., Read, R.: The good, the bad, and the uncertain: contributions of volunteered geographic information to community disaster resilience. Front. Earth Sci. 6, 183 (2018). https://doi.org/10.3389/feart.2018.00183

    Article  Google Scholar 

  41. Varol, O., Ferrara, E., Menczer, F., Flammini, A.: Early detection of promoted campaigns on social media. EPJ Data Sci. 6(1), 1–19 (2017). https://doi.org/10.1140/epjds/s13688-017-0111-y

    Article  Google Scholar 

  42. He, F., Pan, Y., Lin, Q., Miao, X., Chen, Z.: Collective intelligence: a taxonomy and survey. IEEE Access 7, 170213–170225 (2019). https://doi.org/10.1109/ACCESS.2019.2955677

    Article  Google Scholar 

  43. Fisher, E., Pearce, W., Molfino, E.: Politics of Science and Technology (2016). http://www.oxfordbibliographies.com/display/id/obo-9780199756223-0192. https://doi.org/10.1093/obo/9780199756223-0192

  44. Pentzold, C., Fischer, C.: Framing big data: the discursive construction of a radio cell query in Germany. Big Data Soc. 4.0 (2017). https://doi.org/10.1177/2053951717745897

  45. Wu, X., Liao, H.-T.: collective intelligence. In: 2018 IEEE Internet of People, pp. 2005–2010 (2018). https://doi.org/10.1109/SmartWorld.2018.00335

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Acknowledgment

The research is funded by a project of Smart App Design Innovation Research in the Age of New Business, Arts and Engineering Disciplines (2019GXJK186), under the 2019 Guangdong Education Grants, China.

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Correspondence to Zixian Zhou .

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Liao, HT., Zhou, Z., Zhou, Y. (2021). A Systematic Review of Social Media for Intelligent Human-Computer Interaction Research: Why Smart Social Media is Not Enough. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_48

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  • DOI: https://doi.org/10.1007/978-3-030-68449-5_48

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