Who says what about the most-discussed articles of Altmetric?

In Altmetrics, tweets are considered as important potential indicators of immediate social impact of scholarly articles. However, it is still unclear to what extent Twitter captures the actual scholarly impact. Therefore, it is necessary to investigate the people who cite the articles and the content of the tweets with attitude towards the articles comprehensively. In this paper, we combine different indicators to identify opinion leaders in the spread of the articles, and use sentimental analysis to quantify the sentimental polarity of tweets. Altmetrics should highlight the positive role of scientific research results to the public, which is more valuable than simple numbers

Analysis of Tweets Mentioning Scholarly Works from an Institutional Repository

Altmetrics derived from Twitter have potential benefits for institutional repository (IR) stakeholders (faculty, students, administrators, and academic libraries) when metrics aggregators (Altmetric, Plum Analytics) are integrated with IRs. There is limited research on tweets mentioning works in IRs and how the results impact IR stakeholders, specifically libraries. In order to address this gap in the literature, the author conducted a content analysis of tweets tracked by a metrics aggregator (Plum X Metrics) in a Digital Commons IR. The study found that the majority of tweets were neutral in attitude, intended for a general audience, included no hashtags, and were written by users unaffiliated with the works. The results are similar to findings from other studies, including low numbers of tweeted works, high numbers of tweets neutral in attitude, and evidence of self-tweets. The discussion addresses these results in relation to the value of tweets and suggested improvements to Twitter metrics based on IR stakeholders’ needs

Tweeting Library and Information Science: a socio-topical distance analysis

The aim of this paper is to demonstrate how topical distance and social distance can provide meaningful results when analysing scholars’ tweets linking to scholarly publications. To do so, we analyse the social and topical distance between tweeted information science papers and their academic tweeters. This allows us to characterize the tweets of scientific papers, the tweeting behavior of scholars, and the relationship between tweets and citations

Predicting literature’s early impact with sentiment analysis in Twitter

Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. ‘number of unique Twitter users’, improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature’s early impact

Social media exploration for understanding food product attributes perception: the case of coffee and health with Twitter data

Purpose: Food companies and consumers are increasingly interested in healthy food and beverages. Coffee is one of the most commonly consumed beverages worldwide. There is increasing consensus that coffee consumption can have beneficial effects on human body. This paper aims at exploring Twitter messages' content and sentiment towards health attributes of coffee. Design/methodology/approach: The research adopted a utilitarian and hedonic consumer behaviour perspective to analyse online community messages. A sample of 13,000 tweets, from around 4,800 users, that mentions keywords coffee and health was collected on a daily basis for a month in mid-2017. The tweets were categorized with a term frequency analysis, keyword-in-context analysis and sentiment analysis. Findings: Results showed that the majority of tweets are neutral or slightly positive towards coffee’s effects on health. Media and consumers are dynamic Twitter users. Findings support that coffee consumption brings favourable emotions, wellness, energy, positive state of mind and an enjoyable and trendy lifestyle. Many tweets have a positive perception of coffee health benefits, especially relating to mental and physical well-being. Research limitations/implications: The high number of users and tweets analysed compensates the limited amount of time of data collection, Twitter messages' restricted number of characters and quantitative software analysis limitations. Practical implications: The research provides valuable suggestions for food and beverage industry managers. Originality/value: This work adds value to the literature by expanding scholars' research on food product attributes perception analysis by using social media as a source of information. Moreover, it provides valuable information on marketable coffee attributes