Special Issue "Advances in Social Media Analysis"

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

Deadline for manuscript submissions: closed (30 July 2020).

Special Issue Editors

Prof. Giuseppe Fenza
E-Mail Website
Guest Editor
Dipartimento di Scienze Aziendali - Management & Innovation Systems, Università degli Studi di Salerno, 132 - 84084 Fisciano, Italy
Interests: semantic web; fuzzy logic; knowledge extraction; decision making; social media analytics
Dr. Xu An Wang
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

In the era of social media, users have changed the way they communicate, overcoming geographical distances. The shared content is a valuable, dynamic, and continually updated source of information that may be applied to different objectives for addressing marketing strategies, ad targeting, event detection and tracking, and so forth. Nevertheless, the nature of social media contents require Big and Fast Data enabled solutions, and the data value may be carried out only if we are able to filter out dirty and noisy data by also measuring the quality of information.

Over the years, much of the literature about social media analysis has focused on information extraction in order to guide recommender systems in different areas. However, social media contents often convey news, share information during critical events, support a political campaign, and so on. Further, it is sometimes difficult for users to recognize true information or evaluate the trustworthiness of its authors. Thus, it is also crucial to move attention towards these problems and evaluate the impact and new methods against the EU’s 2018 general data protection regulation (GDPR), recent events around information theft, and the role of social media in crime organization activities.

This Special Issue on "Advances in Social Media analysis" aims to collect recent advances in social media data stream analysis. In particular, we would like to collect works aiming to extract knowledge embedded in shared media (text, images, and video) for qualitative filtering, correlating, aggregating, summarizing, and turning data into usable understandable and actionable knowledge.

Potential topics include but are not limited to the following:

  • Quality of information on social media;
  • Information retrieval on social media;
  • Information ranking on social media;
  • Recommender systems;
  • Misinformation and fake news;
  • Social media marketing;
  • Topic detection and tracking;
  • Information summarization;
  • Security and privacy for social network and media;
  • Cloud-based data analysis for social network and media;
  • Social media analysis challenges and applications.

Prof. Giuseppe Fenza
Prof. Xu An Wang
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 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 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 1400 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.

Published Papers (3 papers)

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Research

Open AccessArticle
Intensity of Bilateral Contacts in Social Network Analysis
Information 2020, 11(4), 189; https://doi.org/10.3390/info11040189 - 01 Apr 2020
Viewed by 942
Abstract
The approach presented here introduces the use of directed and weighted graph indicators in order to incorporate the intensity of bilateral contacts. The indicators are tested on a reference email network, and their applicability in explaining the role of each individual in the [...] Read more.
The approach presented here introduces the use of directed and weighted graph indicators in order to incorporate the intensity of bilateral contacts. The indicators are tested on a reference email network, and their applicability in explaining the role of each individual in the organization is explored. The results suggest that directional indicators have high explicatory relevance and can add value to conventional Social Network Analysis (SNA) approaches. Full article
(This article belongs to the Special Issue Advances in Social Media Analysis)
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Open AccessArticle
Multilingual Transformer-Based Personality Traits Estimation
Information 2020, 11(4), 179; https://doi.org/10.3390/info11040179 - 26 Mar 2020
Viewed by 1432
Abstract
Intelligent agents have the potential to understand personality traits of human beings because of their every day interaction with us. The assessment of our psychological traits is a useful tool when we require them to simulate empathy. Since the creation of social media [...] Read more.
Intelligent agents have the potential to understand personality traits of human beings because of their every day interaction with us. The assessment of our psychological traits is a useful tool when we require them to simulate empathy. Since the creation of social media platforms, numerous studies dealt with measuring personality traits by gathering users’ information from their social media profiles. Real world applications showed how natural language processing combined with supervised machine learning algorithms are effective in this field. These applications have some limitations such as focusing on English text only and not considering polysemy in text. In this paper, we propose a multilingual model that handles polysemy by analyzing sentences as a semantic ensemble of interconnected words. The proposed approach processes Facebook posts from the myPersonality dataset and it turns them into a high-dimensional array of features, which are then exploited by a deep neural network architecture based on transformer to perform regression. We prove the effectiveness of our work by comparing the mean squared error of our model with existing baselines and the Kullback–Leibler divergence between the relative data distributions. We obtained state-of-the-art results in personality traits estimation from social media posts for all five personality traits. Full article
(This article belongs to the Special Issue Advances in Social Media Analysis)
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Open AccessArticle
An LSTM Model for Predicting Cross-Platform Bursts of Social Media Activity
Information 2019, 10(12), 394; https://doi.org/10.3390/info10120394 - 14 Dec 2019
Viewed by 954
Abstract
Burst analysis and prediction is a fundamental problem in social network analysis, since user activities have been shown to have an intrinsically bursty nature. Bursts may also be a signal of topics that are of growing real-world interest. Since bursts can be caused [...] Read more.
Burst analysis and prediction is a fundamental problem in social network analysis, since user activities have been shown to have an intrinsically bursty nature. Bursts may also be a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross platform social media data may be valuable for predicting bursts within a single social media platform. A Long-Short-Term-Memory (LSTM) model is proposed in order to capture the temporal dependencies and associations based upon activity information. The data used to test the model was collected from Twitter, Github, and Reddit. Our results show that the LSTM based model is able to leverage the complex cross-platform dynamics to predict bursts. In situations where information gathering from platforms of concern is not possible the learned model can provide a prediction for whether bursts on another platform can be expected. Full article
(This article belongs to the Special Issue Advances in Social Media Analysis)
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