ABSTRACT
Background: Citizen science is a growing practice in which volunteers, including non-scientists, conduct or contribute to research by collecting and analyzing data. The increasing importance of citizen science in the last years has led to an increased interest in detecting how citizen science can contribute to scientific advancements in different areas. Recent research shows that citizen science has become a means of engagement between scientist and the public, encouraging scientific curiosity and promoting scientific knowledge. Methods: In this article, we report on how to apply computational analysis techniques to Twitter messages to reveal the impact of citizen science in health-related areas. The main objectives are (1) to characterize central topics of these discussions, and (2) to identify particularly important actors in these social media networks. Results: For the topics, our findings suggest that sustainable development goals, technologies and health, and COVID-19 are those most addressed by the users. Other topics represented in the data are cancer, public health, mental health, and health and well being of sea and earth living creatures related to sustainable development goals. Conclusion: Based on our results, those entities or actors who are most cited and retweeted are Twitter accounts of projects and not primarily individual professionals or citizen scientists. © The Author(s) 2023.
ABSTRACT
Social media are an integral part of the daily lives of today’s young generation. In addition to the positive impact on learning through these channels, there are also risks related to toxic content like “fake news” on various social media. Fake news aims to change opinions based on disinformation or misinformation supporting conspiracy theories, e.g., related to the pandemic. Fake news creators use various multimedia artifacts, including images taken from serious and valid news sources, to attract the audience’s attention. Tracking images in different contexts can give social media users important clues to distinguish fake news from credible information. We report on the development of a web-based learning environment that includes a “virtual learning companion” to help learners improve their understanding, awareness, and critical thinking concerning such social media threats. The learning environment mimics Instagram and includes toxic and non-toxic content in a controlled way. The companion is implemented as a browser plugin that communicates with students via chat. The companion poses knowledge activation questions and answers according to an underlying script. The companion offers other sources with the same image identified through Reverse Image Search (RIS). The goal is to help learners find the same image in different contexts with different textual descriptions and keywords. For this purpose, we added basic NLP mechanisms to extract keywords from these contexts, including keywords that signal persuasiveness. Currently, we evaluate the impact of this tool and the provided support in distinguishing fake or credible news. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.