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    Public Reaction to Scientific Research via Twitter Sentiment Prediction
    Murtuza Shahzad, Hamed Alhoori
    Journal of Data and Information Science    2022, 7 (1): 97-124.   doi:10.2478/jdis-2022-0003
    Accepted: 06 December 2021

    Abstract79)   HTML22)    PDF (4132KB)(77)      

    Purpose: Social media users share their ideas, thoughts, and emotions with other users. However, it is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications. Additionally, we investigate what features of the research articles help in such prediction. Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles.
    Design/methodology/approach: Several tools are used for sentiment analysis, so we applied five sentiment analysis tools to check which are suitable for capturing a tweet’s sentiment value and decided to use NLTK VADER and TextBlob. We segregated the sentiment value into negative, positive, and neutral. We measure the mean and median of tweets’ sentiment value for research articles with more than one tweet. We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models.
    Findings: We found that the most important feature in all the models was the sentiment of the research article title followed by the author count. We observed that the tree-based models performed better than other classification models, with Random Forest achieving 89% accuracy for binary classification and 73% accuracy for three-label classification.
    Research limitations: In this research, we used state-of-the-art sentiment analysis libraries. However, these libraries might vary at times in their sentiment prediction behavior. Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper’s details. In the future, we intend to broaden the scope of our research by employing word2vec models.
    Practical implications: Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes. Research in this area has relied on fewer and more limited measures, such as citations and user studies with small datasets. There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research. This study will help scientists better comprehend the emotional impact of their work. Additionally, the value of understanding the public’s interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public.
    Originality/value: This study will extend work on public engagement with science, sociology of science, and computational social science. It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.

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    Twitter Users’ Privacy Concerns: What do Their Accounts’ First Names Tell Us?
    Fernandez Espinosa Daniela,Xiao Lu
    Journal of Data and Information Science    2018, 3 (1): 40-53.   doi:10.2478/jdis-2018-0003
    Accepted: 19 March 2018

    Abstract283)   HTML8)    PDF (310KB)(521)      

    Purpose: In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements.

    Design/methodology/approach: We worked with two different data sets to examine whether Twitter users’ gender, inferred from the first name of the account and the profile description, correlates with the privacy setting of the account. We also used a set of features including the inferred gender of Twitter users to develop classifiers that predict user privacy settings.

    Findings: We found that the inferred gender of Twitter users correlates with the account’s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user’s privacy preference.

    Research limitations: It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A major limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, that is, accounts that had names non-existent in the name dictionary, are social bots. It will be interesting to explore the privacy setting of social bots in the Twitter space.

    Practical implications: Companies are investing large amounts of money in business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users’ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users’ provided first names and their profile descriptions, can be used to infer the users’ privacy preference.

    Originality/value: This study explored a new way of inferring Twitter user’s gender, that is, to recognize the user’s gender based on the provided first name and the user’s profile description. The potential of this information for predicting the user’s privacy preference is explored.

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    Library and Information Science Papers Discussed on Twitter: A new Network-based Approach for Measuring Public Attention
    Robin Haunschild, Loet Leydesdorff, Lutz Bornmann
    Journal of Data and Information Science    2020, 5 (3): 5-17.   doi:10.2478/jdis-2020-0017
    Abstract119)   HTML26)    PDF (10634KB)(88)      

    Purpose: In recent years, one can witness a trend in research evaluation to measure the impact on society or attention to research by society (beyond science). We address the following question: can Twitter be meaningfully used for the mapping of public and scientific discourses?

    Design/methodology/approach: Recently, Haunschild et al. (2019) introduced a new network-oriented approach for using Twitter data in research evaluation. Such a procedure can be used to measure the public discussion around a specific field or topic. In this study, we used all papers published in the Web of Science (WoS, Clarivate Analytics) subject category Information Science & Library Science to explore the publicly discussed topics from the area of library and information science (LIS) in comparison to the topics used by scholars in their publications in this area.

    Findings: The results show that LIS papers are represented rather well on Twitter. Similar topics appear in the networks of author keywords of all LIS papers, not tweeted LIS papers, and tweeted LIS papers. The networks of the author keywords of all LIS papers and not tweeted LIS papers are most similar to each other.

    Research limitations: Only papers published since 2011 with DOI were analyzed.

    Practical implications: Although Twitter data do not seem to be useful for quantitative research evaluation, it seems that Twitter data can be used in a more qualitative way for mapping of public and scientific discourses.

    Originality/value: This study explores a rather new methodology for comparing public and scientific discourses.

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    “My ADHD Hellbrain”:A Twitter Data Science Perspective on a Behavioural Disorder
    Mike Thelwall, Meiko Makita, Amalia Mas-Bleda, Emma Stuart
    Journal of Data and Information Science    2021, 6 (1): 13-34.   doi:10.2478/jdis-2021-0007
    Abstract119)   HTML14)    PDF (519KB)(152)      

    Purpose: Attention deficit hyperactivity disorder (ADHD) is a common behavioural condition. This article introduces a new data science method, word association thematic analysis, to investigate whether ADHD tweets can give insights into patient concerns and online communication needs.
    Design/methodology/approach: Tweets matching “my ADHD” (n=58,893) and 99 other conditions (n=1,341,442) were gathered and two thematic analyses conducted. Analysis 1:A standard thematic analysis of ADHD-related tweets. Analysis 2: A word association thematic analysis of themes unique to ADHD.
    Findings: The themes that emerged from the two analyses included people ascribing their brains agency to explain and justify their symptoms and using the concept of neurodivergence for a positive self-image.
    Research limitations: This is a single case study and the results may differ for other topics.
    Practical implications: Health professionals should be sensitive to patients’ needs to understand their behaviour, find ways to justify and explain it to others and to be positive about their condition.
    Originality/value: Word association thematic analysis can give new insights into the (self-reported) patient perspective.

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    Male, Female, and Nonbinary Differences in UK Twitter Self-descriptions: A Fine-grained Systematic Exploration
    Mike Thelwall, Saheeda Thelwall, Ruth Fairclough
    Journal of Data and Information Science    2021, 6 (2): 1-27.   doi:10.2478/jdis-2021-0018
    Abstract264)   HTML131)    PDF (623KB)(500)      

    Purpose: Although gender identities influence how people present themselves on social media, previous studies have tested pre-specified dimensions of difference, potentially overlooking other differences and ignoring nonbinary users.
    Design/methodology/approach: Word association thematic analysis was used to systematically check for fine-grained statistically significant gender differences in Twitter profile descriptions between 409,487 UK-based female, male, and nonbinary users in 2020. A series of statistical tests systematically identified 1,474 differences at the individual word level, and a follow up thematic analysis grouped these words into themes.
    Findings: The results reflect offline variations in interests and in jobs. They also show differences in personal disclosures, as reflected by words, with females mentioning qualifications, relationships, pets, and illnesses much more, nonbinaries discussing sexuality more, and males declaring political and sports affiliations more. Other themes were internally imbalanced, including personal appearance (e.g. male: beardy; female: redhead), self-evaluations (e.g. male: legend; nonbinary: witch; female: feisty), and gender identity (e.g. male: dude; nonbinary: enby; female: queen).
    Research limitations: The methods are affected by linguistic styles and probably under-report nonbinary differences.
    Practical implications: The gender differences found may inform gender theory, and aid social web communicators and marketers.
    Originality/value: The results show a much wider range of gender expression differences than previously acknowledged for any social media site.

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