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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
United Nations General Assembly: Road map for digital cooperation: implementation of the recommendations of the High-level Panel on Digital Cooperation (2020)
Chui, M., Harrysson, M., Manyika, J., Roberts, R.: Applying AI for Social Good. Mckinsey Global Institute (2018)
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
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
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
Anderson, J., Rainie, L., Luchsinger, A.: Artificial Intelligence and the Future of Humans. Pew Research Center (2018)
Garfield, E.: Research fronts. Current Comments (1994)
Clarivate Analytics: Research Areas (Categories/Classification). https://images.webofknowledge.com/WOKRS535R100/help/WOS/hp_research_areas_easca.html. Accessed 01 Nov 2020
Clarivate Analytics: Web of Science categories. https://images.webofknowledge.com/WOKRS535R100/help/WOS/hp_subject_category_terms_tasca.html. Accessed 01 Nov 2020
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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)
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
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
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
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
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
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
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
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
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-68449-5_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68448-8
Online ISBN: 978-3-030-68449-5
eBook Packages: Computer ScienceComputer Science (R0)