Special Issue "Information Analysis and Retrieval in Social Media"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Big Data Mining and Analytics".

Deadline for manuscript submissions: 31 July 2023 | Viewed by 5189

Special Issue Editors

Dr. Lorraine Goeuriot
E-Mail Website
Guest Editor
Laboratoire d'informatique de Grenoble - Equipe MRIM, Université Grenoble Alpes, 38400 Saint-Martin-d'Hères, France
Interests: text retrieval; IR evaluation; opinion mining; natural language processing
Prof. Dr. Gabriella Pasi
E-Mail Website
Guest Editor
Department of Computer Science, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Interests: information retrieval; contextual information access; context modelling; user profiling; social media analytics
Dr. Marco Viviani
E-Mail Website
Guest Editor
Department of Computer Science, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Interests: online data; information analysis and retrieval; social media analysis; social computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are organizing a Special Issue entitled: "Information Analysis and Retrieval in Social Media" in the journal informatics. This is an international peer-reviewed open access journal on information and communication technologies, human–computer interaction, and social informatics and is published quarterly online by MDPI. For detailed information on the journal, please refer to https://www.mdpi.com/journal/informatics.

The multitude of contents that are generated every day through social platforms confront users with the problem of accessing information relevant to their information needs. Over the years, Information Retrieval has proposed various solutions to help users solve this "information overload" problem; however, the characteristics of the content generated through social media require a new phase of analysis and the development of new solutions.

First of all, social content is produced at a speed and in volumes not comparable to those of traditional content disseminated in the form of Web pages. Secondly, the purposes for which users search on social platforms may be different from those of traditional Web search. It must also be taken into account that the aspect of social relations that connect users in virtual communities and the homophily property that distinguishes users can lead to bias in the information received both from custom search engines and from recommendation systems. Another aspect concerns the fact that the credibility of the content disseminated through social media can hardly be verified, and this is an impacting feature when evaluating the relevance of a search result. Finally, traditional evaluation of IR systems, following the Cranfield paradigm, must take into account the social content characteristics through the test collections and metrics used.

The purpose of this Special Issue is therefore to encourage the study and development of Information Retrieval solutions that consider the peculiarities of the social platforms and the contents generated therein to guarantee users’ access to relevant information.

Dr. Lorraine Goeuriot
Prof. Dr. Gabriella Pasi
Dr. Marco Viviani
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. Informatics 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 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

  • information access
  • information retrieval
  • information overload
  • information disorder
  • social media
  • social search
  • evaluations in social search
  • multidimensional relevance.

Published Papers (3 papers)

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Research

Article
Proposal of a Method for the Analysis of Sentiments in Social Networks with the Use of R
Informatics 2022, 9(3), 63; https://doi.org/10.3390/informatics9030063 - 24 Aug 2022
Cited by 1 | Viewed by 676
Abstract
Decision making is vital for the management of all organizations. For this reason, data analysis has become one of the fastest-growing technologies when it comes to generating information and knowledge about data generated by organizations. However, data generation is not limited to traditional [...] Read more.
Decision making is vital for the management of all organizations. For this reason, data analysis has become one of the fastest-growing technologies when it comes to generating information and knowledge about data generated by organizations. However, data generation is not limited to traditional sources. On the contrary, emerging technologies and social networks have become non-traditional sources that provide large volumes of data that can be exploited using different data analysis methods. Here, the objective is to determine the feelings of the population toward a brand, a product, or a service and to even identify the reactions of people to events and trends generated in their environment. Sentiment analysis, for organizations and social groups, has become a necessity that must be covered to identify the acceptance of an idea or its management. Therefore, this work proposes a method for the analysis of sentiment in social networks in such a way that it adapts to the needs of organizations or sectors, and the acceptance or rejection of the population can be efficiently identified from what is exposed in a social network. Full article
(This article belongs to the Special Issue Information Analysis and Retrieval in Social Media)
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Article
An Evaluation Study in Social Media Research: Key Aspects to Enhancing the Promotion of Efficient Organizations on Twitter
Informatics 2021, 8(4), 78; https://doi.org/10.3390/informatics8040078 - 17 Nov 2021
Cited by 1 | Viewed by 1020
Abstract
As social media has shifted from traditional to modern technical patterns, organizations have sought to take advantage of the presence of beneficiaries on social networks. They may serve customers, display ads, and respond to queries on social media accounts such as Twitter. The [...] Read more.
As social media has shifted from traditional to modern technical patterns, organizations have sought to take advantage of the presence of beneficiaries on social networks. They may serve customers, display ads, and respond to queries on social media accounts such as Twitter. The implementation of these services required a scientific study considering: (1) how to attract beneficiaries, (2) attraction times, and (3) measurement of the impact of that attraction. This study aimed to address these three points through an analysis of data from an educational organization’s Twitter account. We found that the interaction rates with tweets increased in the evening, and we identified the best times for the organization to reach more followers. We examined five months of data (an entire semester), analyzing thousands of tweets and their associated impressions, types of responses, integration ratio, and account usage. We also discovered that the quality of tweets had an impact on attracting new followers, particularly when tweeting media such as photos, videos, and other types of content. Finally, this research serves as a resource for educational organizations on new ways to publish accounts and foster organizational growth through electronic media. Full article
(This article belongs to the Special Issue Information Analysis and Retrieval in Social Media)
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Article
An Experimental Analysis of Data Annotation Methodologies for Emotion Detection in Short Text Posted on Social Media
Informatics 2021, 8(1), 19; https://doi.org/10.3390/informatics8010019 - 12 Mar 2021
Cited by 10 | Viewed by 2424
Abstract
Opinion mining techniques, investigating if text is expressing a positive or negative opinion, continuously gain in popularity, attracting the attention of many scientists from different disciplines. Specific use cases, however, where the expressed opinion is indisputably positive or negative, render such solutions obsolete [...] Read more.
Opinion mining techniques, investigating if text is expressing a positive or negative opinion, continuously gain in popularity, attracting the attention of many scientists from different disciplines. Specific use cases, however, where the expressed opinion is indisputably positive or negative, render such solutions obsolete and emphasize the need for a more in-depth analysis of the available text. Emotion analysis is a solution to this problem, but the multi-dimensional elements of the expressed emotions in text along with the complexity of the features that allow their identification pose a significant challenge. Machine learning solutions fail to achieve a high accuracy, mainly due to the limited availability of annotated training datasets, and the bias introduced to the annotations by the personal interpretations of emotions from individuals. A hybrid rule-based algorithm that allows the acquisition of a dataset that is annotated with regard to the Plutchik’s eight basic emotions is proposed in this paper. Emoji, keywords and semantic relationships are used in order to identify in an objective and unbiased way the emotion expressed in a short phrase or text. The acquired datasets are used to train machine learning classification models. The accuracy of the models and the parameters that affect it are presented in length through an experimental analysis. The most accurate model is selected and offered through an API to tackle the emotion detection in social media posts. Full article
(This article belongs to the Special Issue Information Analysis and Retrieval in Social Media)
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