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Future Internet
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Computational Social Science and Natural Language Processing (NLP)
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Computational Social Science and Natural Language Processing (NLP)

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (16 February 2022) | Viewed by 7993

Special Issue Editor

Dr. Bennett Kleinberg

E-Mail Website
Guest Editor
1. Assistant Professor, Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands
2. Honorary Associate Professor, Department of Security and Crime Science, University College London, London, UK
Interests: computational social science; data science; crime science; behavioural science

Special Issue Information

Dear Colleagues,

Text data are becoming increasingly important for research questions of the social and behavioural sciences. Of particular interest are text data produced as natural by-products of human online activity because they may help us study social and behavioural processes through a new lens and on a large scale.

At the same time, the past decade has led to a leap in advancements in natural language processing (NLP), some of which are adopted by computational social scientists in more data-driven investigations. The intersection of computational social and behavioural science and natural language processing thus holds great potential, though it comes with challenges. In this Special Issue, we seek to address the following questions:

  • How can NLP help us to study social and behavioural science research questions and theories?
  • How can approaches, methods, and research designs from the social/behavioural sciences improve NLP research?
  • What are the assumptions we (need to) make about the relationship between language and human behaviour—and how can we test them?

We are interested in contributions covering a wide range of topics and techniques.

Dr. Bennett Kleinberg
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational social science
  • natural language processing
  • behavioural science
  • text mining
  • data science
  • psychology
  • cognitive inference
  • social media studies
  • text as data

Published Papers (2 papers)

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Research

15 pages, 661 KiB  
Article
Dis-Cover AI Minds to Preserve Human Knowledge
by Leonardo Ranaldi, Francesca Fallucchi and Fabio Massimo Zanzotto
Future Internet 2022, 14(1), 10; https://doi.org/10.3390/fi14010010 - 24 Dec 2021
Cited by 18 | Viewed by 2933
Abstract
Modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. The structure of symbols—the operations by which the [...] Read more.
Modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. The structure of symbols—the operations by which the intellectual solution is realized—and the search for strategic reference points evoke important issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped ahead with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we would investigate the syntax as possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning, and human knowledge. Full article
(This article belongs to the Special Issue Computational Social Science and Natural Language Processing (NLP))
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24 pages, 1470 KiB  
Article
Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection
by Anastasia Fedotova, Aleksandr Romanov, Anna Kurtukova and Alexander Shelupanov
Future Internet 2022, 14(1), 4; https://doi.org/10.3390/fi14010004 - 22 Dec 2021
Cited by 8 | Viewed by 3978
Abstract
Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particular, researches of social networks. The article is [...] Read more.
Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particular, researches of social networks. The article is a continuation of the series of studies aimed at the identification of the Russian-language text’s author and reducing the required text volume. The focus of the study was aimed at the attribution of textual data created as a product of human online activity. The effectiveness of the models was evaluated on the two Russian-language datasets: literary texts and short comments from users of social networks. Classical machine learning (ML) algorithms, popular neural networks (NN) architectures, and their hybrids, including convolutional neural network (CNN), networks with long short-term memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and fastText, that have not been used in previous studies, were applied to solve the problem. A particular experiment was devoted to the selection of informative features using genetic algorithms (GA) and evaluation of the classifier trained on the optimal feature space. Using fastText or a combination of support vector machine (SVM) with GA reduced the time costs by half in comparison with deep NNs with comparable accuracy. The average accuracy for literary texts was 80.4% using SVM combined with GA, 82.3% using deep NNs, and 82.1% using fastText. For social media comments, results were 66.3%, 73.2%, and 68.1%, respectively. Full article
(This article belongs to the Special Issue Computational Social Science and Natural Language Processing (NLP))
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