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Some researchers consider most social media communications as public, and posts from networks such as Twitter are routinely harvested and published without anonymization and without direct consent from users. In this chapter, we argue that researchers must move beyond the permissions granted by ‘legal’ accounts of the use of these new forms of data (e.g., Terms and Conditions) to a more nuanced and reflexive ethical approach that puts user expectations, safety, and privacy rights center stage. Through two projects, we present qualitative and quantitative data that illustrate social media users’ views on the use of their data by researchers. Over four in five report expecting to be asked for their consent and nine in ten expect anonymity ahead of publication of their Twitter posts. Given the unique nature of this online public environment and what we know about users’ views pertaining to informed consent, anonymity, and harm, we conclude researchers seeking to embark on social media research should conduct a risk assessment to determine likely privacy infringement and potential user harm from publishing user content.

The purpose of this study is to gain insight into how citations are distributed and concentrated in the introduction, methods, discussion, results and other sections of journal articles to determine which section has received the most citations and whether the citation concentration score affects how articles rank.

The present study uses scite.ai and the Dimensions database to emphasize the significance of including multiple in-text citations in evaluating the impact and quality of journal publications. The study has two approaches: paper-based and author-based.

The study provides empirical insights into how variations in ranking are observed when citation concentration is considered in the evaluation process. It also suggests that in-text citations be used as an evaluation criterion or aspect for assessing the impact and quality of journals, publications and authors.

This study underscores the importance of considering citation concentration when evaluating journal articles. To assess highly cited articles, it suggests using the CC-index method, which is based on scite.ai.