Perspectives and Challenges of AI Techniques in the Field of Social Sciences and Communication

Raul Ramos Pollán


In the past decade, the methods and technologies of artificial intelligence (AI) have made great progress. In many cases, they have become part of the usual landscape of solving new or old problems in different fields of human knowledge. In this progress, there are several aspects, especially three aspects: the availability and universality of data in many fields of human activities; a deeper understanding of the mathematics of the basic control algorithm; and the availability and capability of hardware and computing which allows a wide range and a large number of data experiments. Considering these aspects, the key challenge for each problem and application area is to understand how to use these technologies, to what extent they may reach, and what constraints need to be overcome in order to obtain beneficial results (in terms of production cost, value, etc.). This challenge includes identifying data sources and their integration and recovery requirements, the necessity and cost of acquiring or constructing tag data sets, volume data required for measurement, verifying its feasibility, technical method of data analysis task and its consistency with the final application goal, and social and communication sciences are no exception. The knowledge in these fields is related to artificial intelligence, but they do have particularities that define the most appropriate type of artificial intelligence technology and method (i.e. natural language processing). The successful use of AI technology in these disciplines involves not only technical knowledge, but also the establishment of a viable application environment, including the availability of data, the appropriate complexity of tasks to be performed, and verification procedures with experts in the field. This paper introduces the methodology of generating artificial intelligence model, summarizes the artificial intelligence methods and services most likely to be used in social and communication sciences, and finally gives some application examples to illustrate the practical and technical considerations in this regard.


Artificial Intelligence; Machine Learning; Social Science; Data Science

Full Text:



Moor J. The Dartmouth College artificial intelli-gence conference: The next fifty years. AI Journal 2006; 27(4): 87-91. doi: 10.1609/aimag.v27i4.1911.

Rosenblatt F. The perceptron—A perceiving and recognizing automaton. Presentation. Cornell Aeronautical Laboratory. 1957. doi: 10.1002/9780470694077.ch9.

Olazaran M. A sociological study of the official history of perceptron controversy. Social Studies of Science 1996; 26(3): 611-659. doi: 10.1177/030631296026003005.

Russakovsky O, Deng J, Su H, et al. ImageNet large scale visual recognition challenge. Interna-tional Journal of Computer Vision 2015; 115(3): 211-252. doi: 10.1007/s11263-015-0816-y.

Etter M, Colleoni E, Lllia L, et al. Measuring or-ganizational legitimacy in social media: Assessing citizens’ judgments with sentiment analysis. Business & Society 2018; 57(1): 60-97. doi: 10.1177/0007650316683926.

Alaei A, Becken S, Stantic, B. Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research 2019; 58(2): 175-191. doi: 10.1177/0047287517747753.

Thelwall M. Gender bias in sentiment analysis. Online Information Review 2018; 42(1): 45-57. doi: 10.1108/OIR-05-2017-0139.

Chang YC, Lee FY, Chen CH. A public opinion keyword vector for social sentiment analysis re-search. 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI); 2018 Mar 29–31; Xiamen. IEEE; 2018. p. 752–757.

Gómez-Torres E, Jaimes R, Hidalgo O, et al. (2018). Influence of social networks on the analysis of sentiment applied to the political situation in Ec-uador. Enfoque UTE 2018; 9(1): 67-78.

Arcila-Calderón C, Ortega-Mohedano F, Jimé-nez-Amores J, et al. Análisis supervisado de sen-timientos políticos en español: Clasificación en tiempo real de tweets basada en aprendizaje au-tomático [Supervised political sentiment analysis in Spanish: Real-time twitter classification based on automatic learning]. El profesional de la infor-mación 2017; 26(5): 1699-2407. doi: 10.3145/epi.2017.sep.18.

Stegmeier J, Schünemann WJ, Müller M, et al. Multi method discourse analysis of twitter com-munication: A comparison of two global political issues. In: Scholz R (Editor). Quantifying ap-proaches to discourse for social scientists. Palgrave Macmillan, Cham; 2019. p. 285-314.

Pranav A, Sukiennik N, Hui P. Inflo: News catego-rization and keyphrase extraction for implementa-tion in an aggregation system. 2018.

Ma T. Multi-level relationships between satel-lite-derived nighttime lighting signals and social media-derived human population dynamics. Re-mote sensing 2018; 10(7): 1128. doi: 10.3390/RS10071128.

Diou C,Lelekas P, Delopoulos A. Image-based surrogates of socio-economic status in urban neighborhoods using deep multiple instance learning. Journal of imaging 2018; 4(11): 125. doi: 10.3390/Jimaging4110125.

Bachhety S, Singhal R, Rawat K, et al. Crime de-tection using text recognition and face recognition. International Journal of pure and applied mathe-matics 2018; 119(15): 2797-2807.

Martínez-Camera E, Díaz-Galiano MC, Gar-cía-Cumbreras A, et al. (2017). Overview of TASS 2017. TASS 2017: Workshop on Semantic Analysis at SEPLN; 2017. p. 13-21.



  • There are currently no refbacks.

Copyright (c) 2022 Raul Ramos Pollán

License URL: