HarVis: An integrated social media content analysis framework for YouTube platform

Ahmad, Uzair; Zahid, Anam; Shoaib, Muhammad; Alamri, Atif

doi:10.1016/j.is.2016.10.004

Cited by 20 publications

(13 citation statements)

References 13 publications

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“…Ahmad et al [43] proposed a recursive topic acquisition method for the YouTube platform. The method first collects YouTube search pages based on certain seed queries and extracts seed queries and neighboring words to form a topic vocabulary network.…”

Section: B Topic Acquisitionmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results. INDEX TERMS Document clustering, machine learning, natural language processing, personalized topic search, user log.

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Section: B Topic Acquisitionmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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“…Further reviews of the literature showed that the focus of existing frameworks on social content management models or frameworks fail to consider the main attribute of social media interactions, that of value-co-creation. These existing models and frameworks focused more on the value of the social content to organizations [5], [6], [8], and chose not to emphasize on the co-created value [9]. The existing framework also places less emphasis on the production of service innovations based on managed social content [8], [10].…”

Section: Introductionmentioning

confidence: 99%

“…These existing models and frameworks focused more on the value of the social content to organizations [5], [6], [8], and chose not to emphasize on the co-created value [9]. The existing framework also places less emphasis on the production of service innovations based on managed social content [8], [10]. Besides that, research by [11] also indicates that organizations were focused more on the quality of social content from the technical aspects than the quality of content that meets the needs of customers.…”

Section: Introductionmentioning

confidence: 99%

Developing and Validating an Instrument for Social Content Management

Ahmad

Mukhtar

Yahya

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Social content is important for successful innovation of services. To successfully leverage the value of social content, they need to be properly managed. To achieve this, factors that contribute to social content management must be identified and subsequently validated. In this article, eleven factors that impact upon social content management are highlighted. Taking into consideration the fact that social content management is still an evolving field of study, validation of these factors was done by employing the Delphi technique. Research had indicated that there are several different versions of the Delphi technique, and some researchers have criticized the method as non-rigorous. Thus, in this paper, it is highlighted that this issue could be tackled if the questionnaire instrument used to assess the expert's opinion is developed by adhering to a rigid protocol. Embedded in this rigid protocol, are two measures of validity used which are the content validity ratio (CVR), and the mean of CVR, namely the content validity index (CVi). A total of 86 items are submitted for evaluation and based on the CVR value and comments from the content evaluation panel members, all items are acceptable, with minimal adjustments. The CVi measure returned a value of 0.99, which shows that the factors and the whole questionnaire are accepted. The questionnaire with the validated content could be used as an instrument to validate the factors that are affecting the management of social content. It is hoped that the factors would assist the organization in managing the social content effectively.

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“…In other words, the diverse nature of YouTube comments makes it a potentially valuable source of opinions on videos and the issues depicted in them [ 3 ]. In studies on health-related issues, YouTube qualifies itself as a natural candidate for researchers to study topics in the development of community and data exchange [ 4 ]. YouTube contains a large volume of comments made by video audiences.…”

Section: Introductionmentioning

confidence: 99%

“…They believe that this structure outperforms the traditional approach, such as the bag-of-words model, in terms of mining opinions. HarVis was introduced by Ahmad et al [ 4 ] as a tool to facilitate YouTube content acquisition, data processing, and visualization on any topic. Thelwall [ 3 ] developed comment term frequency comparison in order to investigate YouTube video topics as a novel social media analytics approach.…”

Section: Introductionmentioning

confidence: 99%

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

Teng¹,

Khong²,

Sharif³

et al. 2020

JMIR Public Health Surveill

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Background Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities. Objective The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters’ perceptions and sentiments of healthy eating through text mining techniques. Methods This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure. Results With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily. Conclusions This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

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HarVis: An integrated social media content analysis framework for YouTube platform

Cited by 20 publications

References 13 publications

A Study of Optimizing Search Engine Results Through User Interaction

A Study of Optimizing Search Engine Results Through User Interaction

Developing and Validating an Instrument for Social Content Management

YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

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