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Article
Publication date: 4 September 2019

Yanfen Zhou and Jin-Cheon Na

The purpose of this paper is to understand the similarities and differences between the Twitter users who tweeted on journal articles in psychology and political science…

Abstract

Purpose

The purpose of this paper is to understand the similarities and differences between the Twitter users who tweeted on journal articles in psychology and political science disciplines.

Design/methodology/approach

The data were collected from Web of Science, Altmetric.com, and Twitter. A total of 91,826 tweets with 22,541 distinct Twitter user profiles for psychology discipline and 29,958 tweets with 10,478 distinct Twitter user profiles for political science discipline were used for analysis. The demographics analysis includes gender, geographic location, individual or organization user, academic or non-academic background, and psychology/political science domain knowledge background. A machine learning approach using support vector machine (SVM) was used for user classification based on the Twitter user profile information. Latent Dirichlet allocation (LDA) topic modeling was used to discover the topics that the users discussed from the tweets.

Findings

Results showed that the demographics of Twitter users who tweeted on psychology and political science are significantly different. Tweets on journal articles in psychology reflected more the impact of scientific research finding on the general public and attracted more attention from the general public than the ones in political science. Disciplinary difference in term of user demographics exists, and thus it is important to take the discipline into consideration for future altmetrics studies.

Originality/value

From this study, researchers or research organizations may have a better idea on who their audiences are, and hence more effective strategies can be taken by researchers or organizations to reach a wider audience and enhance their influence.

Details

Online Information Review, vol. 43 no. 7
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 10 April 2017

Tint Hla Hla Htoo and Jin-Cheon Na

The purpose of this paper is to contribute to the understanding of altmetrics in different disciplines of social science: first, by investigating the current richness and future…

1128

Abstract

Purpose

The purpose of this paper is to contribute to the understanding of altmetrics in different disciplines of social science: first, by investigating the current richness and future potential of altmetrics in the selected social science disciplines and then by evaluating the validity of altmetrics as indicators of research impact in each discipline through correlation analysis.

Design/methodology/approach

This study uses three approaches to understand the current richness and future potential of ten altmetric measures in nine selected disciplines: first, investigate the distribution and trend of altmetric data; second, verify the relationship between citation rate and altmetric presence of the discipline using Pearson correlation; and third, perform word frequency analysis on tweets to understand different altmetric presence in different disciplines. In addition, this study uses Spearman and sign test to find the correlation between altmetrics and citation counts for the articles that receive altmetric mention(s) to test the validity of altmetrics as indicators of research impact.

Findings

There is a steady increase in the number of articles that receive altmetric mentions in all disciplines studied. In general, disciplines with higher citation rates have higher altmetric presence. At the same time, altmetrics are also an effective complement to citation in disciplines with low citation rates. There is a moderate correlation with Mendeley and significant but weak correlations with Tweets and CiteULike in seven disciplines. Altmetrics appear effective as a predictor of citation counts in seven out of nine disciplines studied. However, there is low presence and lack of correlation with citation count in business-finance and law disciplines.

Originality/value

This paper furthers the understanding of altmetrics in social science disciplines. It reveals the disciplines where altmetrics are most effective, potentially useful, and fairly applicable. In addition, it presents evidence that altmetrics are an effective complement to citation in disciplines with low citation rates.

Details

Online Information Review, vol. 41 no. 2
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 6 September 2018

Yingxin Estella Ye and Jin-Cheon Na

By analyzing journal articles with high citation counts but low Twitter mentions and vice versa, the purpose of this paper is to provide an overall picture of differences between…

Abstract

Purpose

By analyzing journal articles with high citation counts but low Twitter mentions and vice versa, the purpose of this paper is to provide an overall picture of differences between citation counts and Twitter mentions of academic articles.

Design/methodology/approach

Citation counts from the Web of Science and Twitter mentions of psychological articles under the Social Science Citation Index collection were collected for data analysis. An approach combining both statistical and simple content analysis was adopted to examine important factors contributing to citation counts and Twitter mentions, as well as the patterns of tweets mentioning academic articles.

Findings

Compared to citation counts, Twitter mentions have stronger affiliations with readability and accessibility of academic papers. Readability here was defined as the content size of articles and the usage of jargon and scientific expressions. In addition, Twitter activities, such as the use of hashtags and user mentions, could better facilitate the sharing of articles. Even though discussions of articles or related social phenomena were spotted in the contents of tweets, simple counts of Twitter mentions may not be reliable enough for research evaluations due to issues such as Twitter bots and a deficient understanding of Twitter users’ motivations for mentioning academic articles on Twitter.

Originality/value

This study has elaborated on the differences between Twitter mentions and citation counts by comparing the characteristics of Twitter-inclined and citation-inclined articles. It provides useful information for interested parties who would like to adopt social web metrics such as Twitter mentions as traces of broader engagement with academic literature and potential suggestions to increase the reliability of Twitter metrics. In addition, it gives specific tips for researchers to increase research visibility and get attention from the general public on Twitter.

Details

Online Information Review, vol. 42 no. 7
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 26 January 2022

Xingyu Ken Chen, Jin-Cheon Na, Luke Kien-Weng Tan, Mark Chong and Murphy Choy

The COVID-19 pandemic has spurred a concurrent outbreak of false information online. Debunking false information about a health crisis is critical as misinformation can trigger…

Abstract

Purpose

The COVID-19 pandemic has spurred a concurrent outbreak of false information online. Debunking false information about a health crisis is critical as misinformation can trigger protests or panic, which necessitates a better understanding of it. This exploratory study examined the effects of debunking messages on a COVID-19-related public chat on WhatsApp in Singapore.

Design/methodology/approach

To understand the effects of debunking messages about COVID-19 on WhatsApp conversations, the following was studied. The relationship between source credibility (i.e. characteristics of a communicator that affect the receiver's acceptance of the message) of different debunking message types and their effects on the length of the conversation, sentiments towards various aspects of a crisis, and the information distortions in a message thread were studied. Deep learning techniques, knowledge graphs (KG), and content analyses were used to perform aspect-based sentiment analysis (ABSA) of the messages and measure information distortion.

Findings

Debunking messages with higher source credibility (e.g. providing evidence from authoritative sources like health authorities) help close a discussion thread earlier. Shifts in sentiments towards some aspects of the crisis highlight the value of ABSA in monitoring the effectiveness of debunking messages. Finally, debunking messages with lower source credibility (e.g. stating that the information is false without any substantiation) are likely to increase information distortion in conversation threads.

Originality/value

The study supports the importance of source credibility in debunking and an ABSA approach in analysing the effect of debunking messages during a health crisis, which have practical value for public agencies during a health crisis. Studying differences in the source credibility of debunking messages on WhatsApp is a novel shift from the existing approaches. Additionally, a novel approach to measuring information distortion using KGs was used to shed insights on how debunking can reduce information distortions.

Details

Online Information Review, vol. 46 no. 6
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 23 November 2018

Siyoung Chung, Mark Chong, Jie Sheng Chua and Jin Cheon Na

The purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those…

1314

Abstract

Purpose

The purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments.

Design/methodology/approach

Using a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction.

Findings

The findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users.

Research limitations/implications

Even with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis.

Practical implications

First, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages.

Originality/value

This study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis.

Details

Journal of Communication Management, vol. 23 no. 1
Type: Research Article
ISSN: 1363-254X

Keywords

Article
Publication date: 12 June 2017

Jin-Cheon Na and Yingxin Estella Ye

The purpose of this paper is to provide a comprehensive understanding of scholarly discussions of academic publications on the social web and to further discuss the validity of…

1216

Abstract

Purpose

The purpose of this paper is to provide a comprehensive understanding of scholarly discussions of academic publications on the social web and to further discuss the validity of altmetrics as a research impact assessment tool for academic articles.

Design/methodology/approach

Facebook posts citing psychological journal papers were collected for both quantitative and qualitative analyses. A content analysis approach was adopted to investigate topic preferences and motivations for scholarly discussions among academic and non-academic Facebook users.

Findings

Non-academic users were more actively engaged in scholarly discussions on Facebook than academic users. Among 1,711 Facebook users in the sample, 71.4 percent of them belonged to non-academic users, while 28.6 percent were from an academic background. The Facebook users cited psychological articles with various motivations: discussion and evaluation toward articles (20.4 percent), application to real life practices (16.5 percent), self-promotion (6.4 percent), and data source exchange (6.0 percent). However, nearly half of the posts (50.1 percent) were simply sharing articles without additional user comments. These results implicate that Facebook metric (a count of mentions of a research article on Facebook), as an important source of altmetrics, better reflects the attitudes or perceptions of the general public instead of academia.

Originality/value

This study contributes to the literature by enriching the understanding of Facebook metric as an academic and non-academic impact assessment tool for scientific publication. Through the content analysis of Facebook posts, it also draws insights into the ways in which non-academic audiences are engaging with scholarly outputs.

Details

Online Information Review, vol. 41 no. 3
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 18 January 2008

Jin‐Cheon Na and Hock Leng Neoh

The purpose of this article is to examine the effectiveness of the unified medical language system (UMLS) semantic network as a seed ontology for building a medical field ontology.

Abstract

Purpose

The purpose of this article is to examine the effectiveness of the unified medical language system (UMLS) semantic network as a seed ontology for building a medical field ontology.

Design/methodology/approach

The information extraction process known as the knowledge engineering approach was used to extract concepts and their semantic relations from documents on “colon cancer treatment”. The UMLS semantic network was used as a seed ontology, and was extended and enriched with the extracted concepts and semantic relations using Protégé.

Findings

Only half of the semantic relations extracted manually were defined (or inferable) in the UMLS semantic network. The remaining half could be added to the network to extend its coverage. In addition, two semantic types in the network were found to be too general and four new sublevel semantic types were proposed to make them more specific.

Research limitations/implications

Since only 109 research paper abstracts in the “colon cancer treatment” domain were analyzed in this study, more abstracts from the colon cancer treatment domain as well as from other cancer treatment domains (such as breast cancer treatment) can be analyzed to give a better generalization of our findings.

Originality/value

This study shares our findings on the effectiveness of the UMLS semantic network as a seed ontology for building a medical domain ontology, and also provides the basic guidelines for building or extending a medical domain ontology using the UMLS.

Details

Aslib Proceedings, vol. 60 no. 1
Type: Research Article
ISSN: 0001-253X

Keywords

Article
Publication date: 19 April 2011

Christopher S.G. Khoo, Jin‐Cheon Na and Kokil Jaidka

The purpose of this study is to analyze the macro‐level discourse structure of literature reviews found in information science journal papers, and to identify different styles of…

3003

Abstract

Purpose

The purpose of this study is to analyze the macro‐level discourse structure of literature reviews found in information science journal papers, and to identify different styles of literature review writing. Although there have been several studies of human abstracting, there are hardly any studies of how authors construct literature reviews.

Design/methodology/approach

This study is carried out in the context of a project to develop a summarization system to generate literature reviews automatically. A coding scheme was developed to annotate the high‐level organization of literature reviews, focusing on the types of information. Two sets of annotations were used to check inter‐coder reliability.

Findings

It was found that literature reviews are written in two distinctive styles, with different discourse structures. Descriptive literature reviews summarize individual papers/studies and provide more information on each study, such as research methods, results and interpretation. Integrative literature reviews provide fewer details of individual papers/studies, but focus on ideas and results extracted from these papers. They provide critical summaries of topics, and have a more complex structure of topics and sub‐topics. The reviewer's voice is also more dominant.

Originality/value

The coding scheme is useful for annotating the macro‐level discourse structure of literature reviews, and can be used for studying literature reviews in other fields. The basic characteristics of two styles of literature review writing are identified. The results have provided a foundation for further studies of literature reviews – to identify discourse relations and rhetorical functions employed in literature reviews, and their linguistic expressions.

Details

Online Information Review, vol. 35 no. 2
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 21 June 2011

Luke Kien‐Weng Tan, Jin‐Cheon Na and Yin‐Leng Theng

This study aims to investigate three common approaches – quantitative blog features analysis, content analysis, and community identification – to detect influence in the…

2833

Abstract

Purpose

This study aims to investigate three common approaches – quantitative blog features analysis, content analysis, and community identification – to detect influence in the blogosphere (i.e. among blog posts).

Design/methodology/approach

Quantitative analysis of blog features, together with manual sentiment and agreement analysis and community identification, were performed on blog postings and their content. Correlation studies of the selected influential variables were conducted to determine the effectiveness of each variable.

Findings

Agreement expressed by the linking blogger with the linked blogger, similar sentiments expressed by both bloggers on common topics, and community identity are statistically significant features for detecting influence in the linked blogs.

Research limitations/implications

A small data set of 196 blog posting pairs was used for the study as the blog features and content are analysed manually. Nonetheless statistical analysis on the data set identified significant features that could be used in future studies to automate the influence detection process.

Practical implications

Knowing the effects of blog features and content analysis in detecting influence among blog posts allows a better influence detection method to determine the main chain of information propagation within the blogosphere and the identities of influential bloggers.

Originality/value

The approach of using blog features, content analysis, and community identity provides a comprehensive evaluation of influence in the blogosphere. Unlike previous content analysis approaches that measure document similarity (i.e. common terms) between linked blog posts, our study applies sentiment and agreement analysis to consider the context of the whole blog post content.

Details

Online Information Review, vol. 35 no. 3
Type: Research Article
ISSN: 1468-4527

Keywords

Article
Publication date: 20 April 2010

Jin‐Cheon Na, Tun Thura Thet and Christopher S.G. Khoo

This paper aims to investigate the characteristics and differences in sentiment expression in movie review documents from four online opinion genres – blog postings, discussion…

1789

Abstract

Purpose

This paper aims to investigate the characteristics and differences in sentiment expression in movie review documents from four online opinion genres – blog postings, discussion board threads, user reviews, and critic reviews.

Design/methodology/approach

A collection of movie review documents was harvested from the four types of web sources, and a sample of 520 movie reviews were analysed to compare the content and textual characteristics across the four genres. The analysis focused on document and sentence length, part‐of‐speech distribution, vocabulary, aspects of movies discussed, star ratings used and multimedia content in the reviews. The study also identified frequently occurring positive and negative terms in the different genres, as well as the pattern of responses in discussion threads.

Findings

Critic reviews and blog postings are longer than user reviews and discussion threads, and contain longer sentences. Critic reviews and blogs contain more nouns and prepositions, whereas discussion board and user reviews have more verbs and adverbs. Critic reviews have the largest vocabulary and also the highest proportion of unique terms not found in the other genres. The most informative sentiment words in each genre are provided in the paper. With regard to content, critic reviews are more comprehensive in coverage, and discuss the movie director much more often than the other genres. User reviews discuss the scene aspects (including action and visual effects) more often than the other genres, while blogs tend to talk about the cast, and discuss the music and sound slightly more often.

Research limitations/implications

The study only analysed movie review documents. Similar content and text analysis studies can be carried out in other domains, such as commercial product reviews, celebrity reviews, company reviews and political opinions to compare the results.

Originality/value

The main contribution of the study is the sentiment content analysis results across genres, which show the similarities and differences in content and textual characteristics in the four online opinion genres. The insights will be useful in designing automatic sentiment summarisation methods for multiple online genres.

Details

Online Information Review, vol. 34 no. 2
Type: Research Article
ISSN: 1468-4527

Keywords

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