Special Issue "Social Network and Artificial Intelligence"

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 (30 August 2019) | Viewed by 4276

Special Issue Editors

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is acquiring more and more presence in many contexts. In the news making process, AI can become crucial in all the following phases: from gathering, to production, to editing, and to controlling information.

Forbes Magazine (USA), with its system Narrative Science, generates different informative contents; Associated Press (USA) has robots for the production of news content; The New York Times (USA) produces sports information based on mathematical algorithms; The Los Angeles Times (USA), in March 2014, published a story about an earthquake created by a robot; and, last but not least, in China, two avatars, modeled after popular presenters of flesh and blood, have been made that can inform the audience of information at the last minute. These are just some examples of a myriad of initiatives and projects that try to apply the potential of artificial intelligence to communicative scenarios.

So-called automated journalism, robot journalism, or algorithmic journalism is already a reality and is gaining ground in the writing of traditional and online media; however, it is generating controversy among journalists concerning its possible impact on employment and professional profiles.

In addition, the current availability of big data and the emergence of the so-called "data journalism" makes the human–computing interaction crucial.

Big data, and artificial intelligence applied to journalism, has opened up an infinite range of possibilities that have begun to transform the way content is created, and has grouped data to generate products, managed the diffusion of media, and worked in an indirect and almost personalized way with the public.

This Special Issue aims to present a collection of exciting papers, reporting the most recent advances in techniques for the application of AI to journalism and communication and/or critical reflections of these fields. Possible topics of interest to this Special Issue include but are not limited to the following:

  • The application of AI in any phase of the newsmaking process;
  • The application of AI in big data processing of data, the generation of texts and images, and the dissemination of content;
  • Case studies on the application of AI in the news media;
  • The quality of the contents generated by computers;
  • The deontological and ethical aspects of computer journalism. From control over the text to control over the data;
  • The perverse effects of automation: fake news;
  • New profiles and professional roles;
  • University and artificial intelligence: training challenges;
  • Research and artificial intelligence: the main thematic lines.

Dr. Santiago Tejedor Calvo
Dr. Laura Cervi
Guest Editors

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.


  • artificial intelligence
  • big data
  • journalism
  • innovation
  • human–computer cooperation

Published Papers (1 paper)

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Deep Learning-Based Sentimental Analysis for Large-Scale Imbalanced Twitter Data
Future Internet 2019, 11(9), 190; https://doi.org/10.3390/fi11090190 - 02 Sep 2019
Cited by 10 | Viewed by 3856
Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a [...] Read more.
Emotions detection in social media is very effective to measure the mood of people about a specific topic, news, or product. It has a wide range of applications, including identifying psychological conditions such as anxiety or depression in users. However, it is a challenging task to distinguish useful emotions’ features from a large corpus of text because emotions are subjective, with limited fuzzy boundaries that may be expressed in different terminologies and perceptions. To tackle this issue, this paper presents a hybrid approach of deep learning based on TensorFlow with Keras for emotions detection on a large scale of imbalanced tweets’ data. First, preprocessing steps are used to get useful features from raw tweets without noisy data. Second, the entropy weighting method is used to compute the importance of each feature. Third, class balancer is applied to balance each class. Fourth, Principal Component Analysis (PCA) is applied to transform high correlated features into normalized forms. Finally, the TensorFlow based deep learning with Keras algorithm is proposed to predict high-quality features for emotions classification. The proposed methodology is analyzed on a dataset of 1,600,000 tweets collected from the website ‘kaggle’. Comparison is made of the proposed approach with other state of the art techniques on different training ratios. It is proved that the proposed approach outperformed among other techniques. Full article
(This article belongs to the Special Issue Social Network and Artificial Intelligence)
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