Dataset Information

A Bengali news and public opinion dataset from YouTube.

ABSTRACT: Along with the traditional news publishing policies, news agencies now share news over the internet since people nowadays prefer reading news online. Moreover, news media maintain YouTube channels to publish visual stories. Readers comment to share their opinions below the corresponding news item. These news and comments have been a great source of information and research. However, there is a lack of research in the Bengali news context. This article presents a dataset containing 7,62,678 public comments and replies from 16,016 video news published from 2017 to 2023 from a renowned Bengali news YouTube channel. The data withholds 15 properties of news that include video URL, title, likes, views, date of publishing, hashtags, description, comment author, comment time, comment, likes in the comment, reply author, reply time, reply, and likes in the responses. To ensure privacy, the commentator's name is encoded in the dataset. The dataset is open to use for researchers at https://data.mendeley.com/datasets/3c3j3bkxvn/4. A translated file for the raw dataset is also included. This data may help scholars to identify patterns in public opinion and analyze how public opinion changes over time.

SUBMITTER: Chowdhury LH

PROVIDER: S-EPMC10762352 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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A Bengali news and public opinion dataset from YouTube.

Chowdhury Lomat Haider LH Islam Salekul S Shatabda Swakkhar S

Data in brief 20231210

Along with the traditional news publishing policies, news agencies now share news over the internet since people nowadays prefer reading news online. Moreover, news media maintain YouTube channels to publish visual stories. Readers comment to share their opinions below the corresponding news item. These news and comments have been a great source of information and research. However, there is a lack of research in the Bengali news context. This article presents a dataset containing 7,62,678 publi ...[more]

PMID: 38173982

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Project description:BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

Dataset Information

A Bengali news and public opinion dataset from YouTube.

Publications

A Bengali news and public opinion dataset from YouTube.

Similar Datasets

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