An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining

Manoharan, Saravanapriya; Senthilkumar, Radha

doi:10.1155/2020/3791541

Cited by 19 publications

(9 citation statements)

References 30 publications

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“…By 2015, the information uploaded within one second on the Internet will take five years for a person to read it. Nowadays, the development of the Internet is even faster, so information data are showing a trend of unlimited growth under the background of the Internet era [5]. However, under this trend, the authenticity of network information is extremely unclear, so relevant personnel need people to verify the source of the information and analyze the authenticity of the information content.…”

Section: Reasons For the Rise Of Computer-based Personalizedmentioning

confidence: 99%

Micromedia News Dissemination Impact Assessment Integrated with Personalized Recommendation Algorithm

Guo

2021

Advances in Multimedia

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The development of the Internet has completely changed the way of recommending and disseminating news content. Traditional media forms of news dissemination effectiveness evaluation methods are no longer fully suitable for the evolving needs of new media news dissemination. Therefore, it is necessary to innovate methods for evaluating the effects of new media news dissemination. This article mainly combines personalized recommendation algorithms to evaluate the effectiveness of news dissemination. Currently, popular personalized recommendation algorithms include content-based recommendation algorithms, collaborative filtering recommendation algorithms, knowledge-based recommendation algorithms, and associated recommendation algorithms. These recommendation algorithms are effective. This promotes the dissemination of news, which also recommends news content that is more relevant to user preferences for most users. In addition, the quality of news content is further evaluated through the news rating system, thereby effectively improving the quality of news content.

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Section: Reasons For the Rise Of Computer-based Personalizedmentioning

confidence: 99%

Micromedia News Dissemination Impact Assessment Integrated with Personalized Recommendation Algorithm

Guo

2021

Advances in Multimedia

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“…We rely on free and publicly available third-party data obtained through the WebHose.io 3 Application Programming Interface (API). 4 The WebHose API has gained popularity in a variety of recent Web studies ranging from assessing population awareness about scientific innovations (Monsores et al 2020) to investigating news virality in social media (Manoharan and Senthilkumar 2020). The API collects regularly updated unstructured content from the Web including blogs, news articles, and associated online discussions such as comments, reviews, and message boards.…”

Section: Methodsmentioning

confidence: 99%

Characterizing Online Media on COVID-19 during the Early Months of the Pandemic

Dambanemuya¹,

Lin

Horvát³

2021

JQD

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The 2019 coronavirus disease had wide-ranging effects on public health throughout the world. Vital in managing its spread was effective communication about public health guidelines such as social distancing and sheltering in place. Our study provides a descriptive analysis of online information sharing about coronavirus-related topics in 5.2 million English-language news articles, blog posts, and discussion forum entries shared in 197 countries during the early months of the pandemic. We illustrate potential approaches to analyze the data while emphasizing how often-overlooked dimensions of the online media environment play a crucial role in the observed information-sharing patterns. In particular, we show how the following three dimensions matter: (1) online media posts’ geographic location in relation to local exposure to the virus; (2) the platforms and types of media chosen for discussing various topics; and (3) temporal variations in information-sharing patterns. Our descriptive analyses of the multimedia data suggest that studies that overlook these crucial aspects of online media may arrive at misleading conclusions about the observed information-sharing patterns. This could impact the success of potential communication strategies devised based on data from online media. Our work has broad implications for the study and design of computational approaches for characterizing large-scale information dissemination during pandemics and beyond.

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“…Jiang et al (2020) proposed a hybrid recommendation model with user-post interaction ratings, implicit feedback and auxiliary information using deep emotion analysis and multi-source view fusion. Manoharan and Senthilkumar (2020) introduced a fuzzy logic approach to predict users' diversified interest and its categories by analyzing their implicit user profiles and recommended fresh news articles from social media based on users' interest categories. A framework of recommender system that uses social network and social context information was proposed to suggest diabetes accounts in micro-blog (Yang et al, 2017a).…”

Section: Social Informationmentioning

confidence: 99%

Personalized content recommendation in online health communities

Yang

Gao

2021

IMDS

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PurposeRecommending suitable content for users of online health communities (OHCs) is critical for overcoming information overload problem and facilitate medical decision making, but remains not fully investigated. This study aims to provide a content recommendation approach to automatically match valuable health-related information for OHC members.Design/methodology/approachA framework of health-related content recommendation was proposed by leveraging rich social information in online communities. The authors constructed user influence relationship (UIR) utilizing users' interaction records, user profiles and user-generated content. The initial user rating matrix and the user post matching matrix were then created by analyzing text content of posts. Finally, the user rating matrix and the recommended content were generated for community members. Datasets were collected from an OHC to evaluate the effectiveness of the proposed approach.FindingsThe experimental results revealed that the proposed method statistically outperformed baseline models in content recommendation for users of OHCs.Research limitations/implicationsThe incorporation of social information can significantly enhance the performance of content recommendation in OHCs. The user post matching degree based on text analysis can improve the effectiveness of recommendation.Practical implicationsThis study potentially contributes to the social support exchange and medical decision making of community members and the sustainable prosperity of OHCs.Originality/valueThis study proposes a novel social content recommendation method for online health consumers based on UIRs by leveraging social information in OHCs. The results indicate the significance of social information in content recommendation of healthcare social media.

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An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining

Cited by 19 publications

References 30 publications

Micromedia News Dissemination Impact Assessment Integrated with Personalized Recommendation Algorithm

Micromedia News Dissemination Impact Assessment Integrated with Personalized Recommendation Algorithm

Characterizing Online Media on COVID-19 during the Early Months of the Pandemic

Personalized content recommendation in online health communities

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