Search

Purpose: The Method section of research articles offers an important space for researchers to describe their research processes and research objects they utilize. To understand the relationship between these research materials and their representations in scientific publications, this paper offers a quantitative examination of the citation contexts of the most frequently cited references in the Method section of the paper sample, many of which belong to the category of research material objects. Design/methodology/approach: In this research, the authors assessed the extent to which these references appear in the Method section, which is regarded as an indicator of the instrumentality of the reference. The authors also examined how this central measurement is connected to its other citation contexts, such as key linguistic attributes and verbs that are used in citation sentences. Findings: The authors found that a series of key linguistic attributes can be used to predict the instrumentality of a reference. The use of self-mention phrases and the readability score of the citances are especially strong predictors, along with boosters and hedges, the two measurements that were not included in the final model. Research limitations/implications: This research focuses on a single research domain, psychology, which limits the understanding of how research material objects are cited in different research domains or interdisciplinary research contexts. Moreover, this research is based on 200 frequently cited references, which are unable to represent all references cited in psychological publications. Practical implications: With the identified relationship between instrumental citation contexts and other characteristics of citation sentences, this research opens the possibility of more accurately identifying research material objects from scientific references, the most accessible scholarly data. Originality/value: This is the first large-scale, quantitative analysis of the linguistic features of citations to research material objects. This study offers important baseline results for future studies focusing on scientific instruments, an increasingly important type of object involved in scientific research. Peer review: The peer review history for this article is available at: 10.1108/OIR-03-2021-0171 [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females.Design/methodology/approach This paper uses data sets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females.Findings Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis.Research limitations/implications Only one lexical sentiment analysis algorithm was used.Practical implications Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results.Originality/value This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.Design/methodology/approach In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.Findings Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.Originality/value This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc. [ABSTRACT FROM AUTHOR]

Purpose Social network sites have been widely adopted by politicians in the last election campaigns. To increase the effectiveness of these campaigns the potential electorate is to be identified, as targeted ads are much more effective than non-targeted ads. Therefore, the purpose of this paper is to propose and implement a new methodology for automatic prediction of political orientation of users on social network sites by comparison to texts from the overtly political parties’ pages.Design/methodology/approach To this end, textual information on personal users’ pages is used as a source of statistical features. The authors apply automatic text categorization algorithms to distinguish between texts of users from different political wings. However, these algorithms require a set of manually labeled texts for training, which is typically unavailable in real life situations. To overcome this limitation the authors propose to use texts available on various political parties’ pages on a social network site to train the classifier. The political leaning of these texts is determined by the political affiliation of the corresponding parties. The classifier learned on such overtly political texts is then applied on the personal user pages to predict their political orientation. To assess the validity and effectiveness of the proposed methodology two corpora were constructed: personal Facebook pages of 450 Israeli citizens, and political parties Facebook pages of the nine prominent Israeli parties.Findings The authors found that when a political tendency classifier is trained and tested on data in the same corpus, accuracy is very high. More significantly, training on manifestly political texts (political party Facebook pages) yields classifiers which can be used to classify non-political personal Facebook pages with fair accuracy.Social implications Previous studies have shown that targeted ads are more effective than non-targeted ads leading to substantial saving in the advertising budget. Therefore, the approach for automatic determining the political orientation of users on social network sites might be adopted for targeting political messages, especially during election campaigns.Originality/value This paper proposes and implements a new approach for automatic cross-corpora identification of political bias of user profiles on social network. This suggests that individuals’ political tendencies can be identified without recourse to any tagged personal data. In addition, the authors use learned classifiers to determine which self-identified centrists lean left or right and which voters are likely to switch allegiance in subsequent elections. [ABSTRACT FROM AUTHOR]

Purpose - Based on the premises of Putnam?s bridging social capital, and on Ellison, Steinfield and Lampe?s notion of maintained social capital, the purpose of this paper is to explore the extent to which the well-being variables of self-esteem and loneliness, as well as What?s App attitudes and intention to use variables, explain the social capital students gain from What?s App use. Design/methodology/approach - The research was conducted in Israel during the second semester of the 2014 academic year and included 124 students from two major universities in Israel. Researchers used six questionnaires to gather data. Findings - Findings confirm that the well-being variables, as well as What?s App attitudes and intention to use, affect the social capital students gain while using What?s App. Originality/value - The findings of this study shed light on a new technological platform: What?s App that has rarely been examined to date. In addition, it expands the social capital and well-being perspectives to new media. [ABSTRACT FROM AUTHOR]