Special Issue "Love & Hate in the Time of Social Media and Social Networks"
Deadline for manuscript submissions: 15 February 2018
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. Viviana Patti
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 papers will be 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 quarterly 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 850 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.
- Emotion analysis
- Affective computing
- Sentiment analysis
- Hate speech detection
- Opinion Mining
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: TwitPersonality: Computing Personality Traits from Tweets using Word Embeddings and Supervised Learning
Author: Giuseppe Rizzo (Istituto Superiore Mario Boella Innovation Development)
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 buddy lists, interests of musics and movies, endorsements and likes an individual has ever performed. Turning these information into signals and given them as inputs to supervised learning approaches has resulted to be 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 the access restrictions make them unfeasible to be used in a real in-situ scenario. In this paper we propose a supervised learning approach to compute personality dimensions by only relying on what an individual tweets about his thoughts. 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 feasibility and the effectiveness of the approach by measuring the precision tested against an international benchmark. We also show how this approach can be used for personalizing any types of offers (tourist and musical) and be used as feature for weighting individuals' opinions and will.