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Love & Hate in the Time of Social Media and...
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Love & Hate in the Time of Social Media and Social Networks

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 March 2018) | Viewed by 29804

Special Issue Editors

Dr. Carlos A. Iglesias

E-Mail Website
Guest Editor
Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: multiagent systems; social computing; linked data; natural language processing; affect technology; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Viviana Patti

E-Mail Website
Guest Editor
Dipartimento di Informatica, Corso Svizzera 185, 10149 Torino (Italy), Università di Torino
Interests: natural language processing; affective computing; sentiment analysis; irony detection; social semantic web

Special Issue Information

Dear Colleagues,

Social networks are becoming more and more present in our daily life. According to the Global Web Index, digital consumers spend an average of 2 hours a day on social media and messaging. Thus, a large body of research has been developed in the last years to process automatically social media and social networks, with the aim of understanding, discovering insights and exploiting this information. All this has contributed to the development of research areas such as sentiment analysis and social network analysis. The new communication media offer a unique opportunity to observe "in the wild" feelings and reactions spontaneously expressed on different topics, often using figurative language: sarcastic messages can be the ones that spread more virulently.

Nowadays the focus of research is moving from polarity classification to more advanced and fine-grained aspects, which can reveal insights on users’ emotions or personality traits, or to their specific stance towards a target in online political debates, where also the presence of hate speech is an important issue to monitor, for preventing interference with other rights and the occasioning of certain harms. Temporal evolution of opinions in online communities is also a hot research topic, which calls for a combination of sentiment and social network analysis techniques.

The aim of this special issue is to put together contributions that analyze strong feelings, such as love or hate in social media and social networks.

Topics of interest include, but are not limited to, the following topics:

  • Detection of opinions in debates on controversial topics
  • Emotion diffusion in social networks and social media
  • Emotion analysis techniques
  • Affective ontologies
  • Multimodal sentiment and emotion analysis
  • Time evolving opinion and sentiment analysis
  • Hate speech detection in social media
  • Stance detection in social media
  • Figurative language and sarcasm in online debates
  • Applications of emotion aware techniques
Dr. Carlos A. Iglesias
Dr. Viviana Patti
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. Information 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

  • Emotion analysis
  • Affective computing
  • Sentiment analysis
  • Hate speech detection
  • Opinion Mining

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Published Papers (4 papers)

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Editorial

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2 pages, 148 KiB  
Editorial
Editorial for the Special Issue on “Love & Hate in the Time of Social Media and Social Networks”
by Carlos A. Iglesias and Viviana Patti
Information 2018, 9(8), 185; https://doi.org/10.3390/info9080185 - 25 Jul 2018
Cited by 2 | Viewed by 3722
Abstract
n/a Full article
(This article belongs to the Special Issue Love & Hate in the Time of Social Media and Social Networks)

Research

Jump to: Editorial

20 pages, 436 KiB  
Article
TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning
by Giulio Carducci, Giuseppe Rizzo, Diego Monti, Enrico Palumbo and Maurizio Morisio
Information 2018, 9(5), 127; https://doi.org/10.3390/info9050127 - 18 May 2018
Cited by 59 | Viewed by 13698
Abstract
We are what we do, like, and say. Numerous research efforts have been pushed towards the automatic assessment of personality dimensions relying on a set of information gathered from social media platforms such as list of friends, interests of musics and movies, endorsements [...] Read more.
We are what we do, like, and say. Numerous research efforts have been pushed towards the automatic assessment of personality dimensions relying on a set of information gathered from social media platforms such as list of friends, interests of musics and movies, endorsements and likes an individual has ever performed. Turning this information into signals and giving them as inputs to supervised learning approaches has resulted in being particularly effective and accurate in computing personality traits and types. Despite the demonstrated accuracy of these approaches, the sheer amount of information needed to put in place such a methodology and access restrictions make them unfeasible to be used in a real usage scenario. In this paper, we propose a supervised learning approach to compute personality traits by only relying on what an individual tweets about publicly. The approach segments tweets in tokens, then it learns word vector representations as embeddings that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the mean squared error of the learned model using an international benchmark of Facebook status updates. We also test the transfer learning predictive power of this model with an in-house built benchmark created by twenty four panelists who performed a state-of-the-art psychological survey and we observe a good conversion of the model while analyzing their Twitter posts towards the personality traits extracted from the survey. Full article
(This article belongs to the Special Issue Love & Hate in the Time of Social Media and Social Networks)
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13 pages, 461 KiB  
Article
A Comparison of Emotion Annotation Approaches for Text
by Ian D. Wood, John P. McCrae, Vladimir Andryushechkin and Paul Buitelaar
Information 2018, 9(5), 117; https://doi.org/10.3390/info9050117 - 11 May 2018
Cited by 9 | Viewed by 6522
Abstract
While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of a more nuanced affect has received less attention: there are few publicly available annotated resources and there are a [...] Read more.
While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of a more nuanced affect has received less attention: there are few publicly available annotated resources and there are a number of competing emotion representation schemes with as yet no clear approach to choose between them. To address this lack, we present a series of emotion annotation studies on tweets, providing methods for comparisons between annotation methods (relative vs. absolute) and between different representation schemes. We find improved annotator agreement with a relative annotation scheme (comparisons) on a dimensional emotion model over a categorical annotation scheme on Ekman’s six basic emotions; however, when we compare inter-annotator agreement for comparisons with agreement for a rating scale annotation scheme (both with the same dimensional emotion model), we find improved inter-annotator agreement with rating scales, challenging a common belief that relative judgements are more reliable. To support these studies and as a contribution in itself, we further present a publicly available collection of 2019 tweets annotated with scores on each of four emotion dimensions: valence, arousal, dominance and surprise, following the emotion representation model identified by Fontaine et al. in 2007. Full article
(This article belongs to the Special Issue Love & Hate in the Time of Social Media and Social Networks)
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13 pages, 1090 KiB  
Article
Towards Aiding Decision-Making in Social Networks by Using Sentiment and Stress Combined Analysis
by Guillem Aguado, Vicente Julian and Ana Garcia-Fornes
Information 2018, 9(5), 107; https://doi.org/10.3390/info9050107 - 2 May 2018
Cited by 5 | Viewed by 4770
Abstract
The present work is a study of the detection of negative emotional states that people have using social network sites (SNSs), and the effect that this negative state has on the repercussions of posted messages. We aim to discover in which grade a [...] Read more.
The present work is a study of the detection of negative emotional states that people have using social network sites (SNSs), and the effect that this negative state has on the repercussions of posted messages. We aim to discover in which grade a user having an affective state considered negative by an Analyzer can affect other users and generate bad repercussions. Those Analyzers that we propose are a Sentiment Analyzer, a Stress Analyzer and a novel combined Analyzer. We also want to discover what Analyzer is more suitable to predict a bad future situation, and in what context. We designed a Multi-Agent System (MAS) that uses different Analyzers to protect or advise users. This MAS uses the trained and tested Analyzers to predict future bad situations in social media, which could be triggered by the actions of a user that has an emotional state considered negative. We conducted an experimentation with different datasets of text messages from Twitter.com to examine the ability of the system to predict bad repercussions, by comparing the polarity, stress level or combined value classification of the messages that are replies to the ones of the messages that originated them. Full article
(This article belongs to the Special Issue Love & Hate in the Time of Social Media and Social Networks)
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