Editorial

3 pages, 187 KiB

Open AccessEditorial

Information Retrieval and Social Media Mining

by María N. Moreno-García

Information 2020, 11(12), 578; https://0-doi-org.brum.beds.ac.uk/10.3390/info11120578 - 11 Dec 2020

Cited by 3 | Viewed by 2345

The large amount of digital content available through web sites, social networks, streaming services, and other distribution media, allows more and more people to access virtually unlimited sources of information, products, and services [...] Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

Research

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13 pages, 452 KiB

Open AccessArticle

Penalty-Enhanced Utility-Based Multi-Criteria Recommendations

by Yong Zheng

Information 2020, 11(12), 551; https://0-doi-org.brum.beds.ac.uk/10.3390/info11120551 - 26 Nov 2020

Cited by 3 | Viewed by 2059

Abstract

Recommender systems have been successfully applied to assist decision making in multiple domains and applications. Multi-criteria recommender systems try to take the user preferences on multiple criteria into consideration, in order to further improve the quality of the recommendations. Most recently, the utility-based [...] Read more.

Recommender systems have been successfully applied to assist decision making in multiple domains and applications. Multi-criteria recommender systems try to take the user preferences on multiple criteria into consideration, in order to further improve the quality of the recommendations. Most recently, the utility-based multi-criteria recommendation approach has been proposed as an effective and promising solution. However, the issue of over-/under-expectations was ignored in the approach, which may bring risks to the recommendation model. In this paper, we propose a penalty-enhanced model to alleviate this issue. Our experimental results based on multiple real-world data sets can demonstrate the effectiveness of the proposed solutions. In addition, the outcomes of the proposed solution can also help explain the characteristics of the applications by observing the treatment on the issue of over-/under-expectations. Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

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15 pages, 712 KiB

Open AccessArticle

Sentiment Analysis and Text Mining of Questionnaires to Support Telemonitoring Programs

by Chiara Zucco, Clarissa Paglia, Sonia Graziano, Sergio Bella and Mario Cannataro

Information 2020, 11(12), 550; https://0-doi-org.brum.beds.ac.uk/10.3390/info11120550 - 26 Nov 2020

Cited by 7 | Viewed by 2831

Abstract

While several studies have shown how telemedicine and, in particular, home telemonitoring programs lead to an improvement in the patient’s quality of life, a reduction in hospitalizations, and lower healthcare costs, different variables may affect telemonitoring effectiveness and purposes. In the present paper, [...] Read more.

While several studies have shown how telemedicine and, in particular, home telemonitoring programs lead to an improvement in the patient’s quality of life, a reduction in hospitalizations, and lower healthcare costs, different variables may affect telemonitoring effectiveness and purposes. In the present paper, an integrated software system, based on Sentiment Analysis and Text Mining, to deliver, collect, and analyze questionnaire responses in telemonitoring programs is presented. The system was designed to be a complement to home telemonitoring programs with the objective of investigating the paired relationship between opinions and the adherence scores of patients and their changes through time. The novel contributions of the system are: (i) the design and software prototype for the management of online questionnaires over time; and (ii) an analysis pipeline that leverages a sentiment polarity score by using it as a numerical feature for the integration and the evaluation of open-ended questions in clinical questionnaires. The software pipeline was initially validated with a case-study application to discuss the plausibility of the existence of a directed relationship between a score representing the opinion polarity of patients about telemedicine, and their adherence score, which measures how well patients follow the telehomecare program. In this case-study, 169 online surveys sent by 38 patients enrolled in a home telemonitoring program provided by the Cystic Fibrosis Unit at the “Bambino Gesù” Children’s Hospital in Rome, Italy, were collected and analyzed. The experimental results show that, under a Granger-causality perspective, a predictive relationship may exist between the considered variables. If supported, these preliminary results may have many possible implications of practical relevance, for instance the early detection of poor adherence in patients to enable the application of personalized and targeted actions. Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

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21 pages, 2129 KiB

Open AccessArticle

Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

by Alejandro Ramón-Hernández, Alfredo Simón-Cuevas, María Matilde García Lorenzo, Leticia Arco and Jesús Serrano-Guerrero

Information 2020, 11(11), 535; https://0-doi-org.brum.beds.ac.uk/10.3390/info11110535 - 18 Nov 2020

Cited by 2 | Viewed by 2691

Abstract

Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the [...] Read more.

Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model. Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

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13 pages, 348 KiB

Open AccessArticle

Graph Convolutional Neural Network for a Pharmacy Cross-Selling Recommender System

by Franz Hell, Yasser Taha, Gereon Hinz, Sabine Heibei, Harald Müller and Alois Knoll

Information 2020, 11(11), 525; https://0-doi-org.brum.beds.ac.uk/10.3390/info11110525 - 11 Nov 2020

Cited by 10 | Viewed by 4811

Abstract

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance in recommender system benchmarks. Adapting these methods to pharmacy product cross-selling recommendation tasks with a million products and hundreds of millions of sales remains a challenge, due to the [...] Read more.

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance in recommender system benchmarks. Adapting these methods to pharmacy product cross-selling recommendation tasks with a million products and hundreds of millions of sales remains a challenge, due to the intricate medical and legal properties of pharmaceutical data. To tackle this challenge, we developed a graph convolutional network (GCN) algorithm called PharmaSage, which uses graph convolutions to generate embeddings for pharmacy products, which are then used in a downstream recommendation task. In the underlying graph, we incorporate both cross-sales information from the sales transaction within the graph structure, as well as product information as node features. Via modifications to the sampling involved in the network optimization process, we address a common phenomenon in recommender systems, the so-called popularity bias: popular products are frequently recommended, while less popular items are often neglected and recommended seldomly or not at all. We deployed PharmaSage using real-world sales data and trained it on 700,000 articles represented as nodes in a graph with edges between nodes representing approximately 100 million sales transactions. By exploiting the pharmaceutical product properties, such as their indications, ingredients, and adverse effects, and combining these with large sales histories, we achieved better results than with a purely statistics based approach. To our knowledge, this is the first application of deep graph embeddings for pharmacy product cross-selling recommendation at this scale to date. Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

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16 pages, 248 KiB

Open AccessArticle

Social Capital on Social Media—Concepts, Measurement Techniques and Trends in Operationalization

by Flora Poecze and Christine Strauss

Information 2020, 11(11), 515; https://0-doi-org.brum.beds.ac.uk/10.3390/info11110515 - 04 Nov 2020

Cited by 7 | Viewed by 7091

Abstract

The introduction of the Web 2.0 era and the associated emergence of social media platforms opened an interdisciplinary research domain, wherein a growing number of studies are focusing on the interrelationship of social media usage and perceived individual social capital. The primary aim [...] Read more.

The introduction of the Web 2.0 era and the associated emergence of social media platforms opened an interdisciplinary research domain, wherein a growing number of studies are focusing on the interrelationship of social media usage and perceived individual social capital. The primary aim of the present study is to introduce the existing measurement techniques of social capital in this domain, explore trends, and offer promising directions and implications for future research. Applying the method of a scoping review, a set of 80 systematically identified scientific publications were analyzed, categorized, grouped and discussed. Focus was placed on the employed viewpoints and measurement techniques necessary to tap into the possible consistencies and/or heterogeneity in this domain in terms of operationalization. The results reveal that multiple views and measurement techniques are present in this research area, which might raise a challenge in future synthesis approaches, especially in the case of future meta-analytical contributions. Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

14 pages, 506 KiB

Open AccessArticle

Document Recommendations and Feedback Collection Analysis within the Slovenian Open-Access Infrastructure

by Mladen Borovič, Marko Ferme, Janez Brezovnik, Sandi Majninger, Klemen Kac and Milan Ojsteršek

Information 2020, 11(11), 497; https://0-doi-org.brum.beds.ac.uk/10.3390/info11110497 - 23 Oct 2020

Cited by 4 | Viewed by 1967

Abstract

This paper presents a hybrid document recommender system intended for use in digital libraries and institutional repositories that are part of the Slovenian Open Access Infrastructure. The recommender system provides recommendations of similar documents across different digital libraries and institutional repositories with the [...] Read more.

This paper presents a hybrid document recommender system intended for use in digital libraries and institutional repositories that are part of the Slovenian Open Access Infrastructure. The recommender system provides recommendations of similar documents across different digital libraries and institutional repositories with the aim to connect researchers and improve collaboration efforts. The hybrid recommender system makes use of document processing techniques, document metadata, and the similarity ranking function BM25 to provide content-based recommendations as a primary method. It also uses collaborative-filtering methods as a secondary method in a cascade hybrid recommendation technique. We also provide a real-world data feedback collection analysis for our hybrid recommender system on an academic digital repository in order to be able to identify suitable time-frames for direct feedback collection during the year. Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

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14 pages, 3567 KiB

Open AccessArticle

Detecting and Tracking Significant Events for Individuals on Twitter by Monitoring the Evolution of Twitter Followership Networks

by Tao Tang and Guangmin Hu

Information 2020, 11(9), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/info11090450 - 16 Sep 2020

Cited by 4 | Viewed by 2240

Abstract

People publish tweets on Twitter to share everything from global news to their daily life. Abundant user-generated content makes Twitter become one of the major channels for people to obtain information about real-world events. Event detection techniques help to extract events from massive [...] Read more.

People publish tweets on Twitter to share everything from global news to their daily life. Abundant user-generated content makes Twitter become one of the major channels for people to obtain information about real-world events. Event detection techniques help to extract events from massive amounts of Twitter data. However, most existing techniques are based on Twitter information streams, which contain plenty of noise and polluted content that would affect the accuracy of the detecting result. In this article, we present an event discovery method based on the change of the user’s followers, which can detect the occurrences of significant events relevant to the particular user. We divide these events into categories according to the positive or negative effect on the specific user. Further, we observe the evolution of individuals’ followership networks and analyze the dynamics of networks. The results show that events have different effects on the evolution of different features of Twitter followership networks. Our findings may play an important role for realizing how patterns of social interaction are impacted by events and can be applied in fields such as public opinion monitoring, disaster warning, crisis management, and intelligent decision making. Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

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16 pages, 2461 KiB

Open AccessArticle

Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems

by Diego Sánchez-Moreno, Vivian López Batista, M. Dolores Muñoz Vicente, Ángel Luis Sánchez Lázaro and María N. Moreno-García

Information 2020, 11(9), 439; https://0-doi-org.brum.beds.ac.uk/10.3390/info11090439 - 11 Sep 2020

Cited by 17 | Viewed by 3293

Abstract

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to [...] Read more.

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and social influence. The fact that streaming music platforms have some social functionalities also allows this type of information to be used for music recommendation. In this work, we take advantage of the friendship structure to address a type of recommendation bias derived from the way collaborative filtering methods compute the neighborhood. These methods restrict the rating predictions for a user to the items that have been rated by their nearest neighbors while leaving out other items that might be of his/her interest. This problem is different from the popularity bias caused by the power-law distribution of the item rating frequency (long-tail), well-known in the music domain, although both shortcomings can be related. Our proposal is based on extending and diversifying the neighborhood by capturing trust and homophily effects between users through social structure metrics. The results show an increase in potentially recommendable items while reducing recommendation error rates. Full article

(This article belongs to the Special Issue Information Retrieval and Social Media Mining)

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