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Disinformation currently floods the Internet worldwide and likely affects social knowledge building in online communities. Strategic framing in the form of emotional, value, and semantic framing is a common tool fake news producers use to more efficiently disseminate their content, yet these strategies have not been sufficiently examined, even less in relationship with the online dialog initiated by misinformation. In this exploratory study we aim to investigate the most relevant types of strategic framing and their role as predictors of news consumers' emotions, argumentation, and social knowledge building in the online dialog on a German alternative news site. Employing both manual and automated content analysis, we found significant relationships between framing in posted news articles and the subsequent online dialog. News framing predicted negative emotions in the online discussions and interfered with argumentation and social knowledge building. Conclusions pertain to digital information literacy interventions and further research on news framing. • This study combines framing theory and dialogism with disinformation content. • We utilized both manual and automatic content analysis to discover prominent frames in fake news content. • We analyzed roughly 1500 comments from a German fake news site. • Framing predicted social knowledge building in the sense of dialogism and might have led to an echo chamber environment. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has changed the entire world, while the impact and usage of online learning environments has greatly increased. This paper presents a new version of the ReaderBench framework, grounded in Cohesion Network Analysis, which can be used to evaluate the online activity of students as a plug-in feature to Moodle. A Recurrent Neural Network with LSTM cells that combines global features, including participation and initiation indices, with a time series analysis on timeframes is used to predict student grades, while multiple sociograms are generated to observe interaction patterns. Students' behaviors and interactions are compared before and during COVID-19 using two consecutive yearly instances of an undergraduate course in Algorithm Design, conducted in Romanian using Moodle. The COVID-19 outbreak generated an off-balance, a drastic increase in participation, followed by a decrease towards the end of the semester, compared to the academic year 2018–2019 when lower fluctuations in participation were observed. The prediction model for the 2018–2019 academic year obtained an R 2 of 0.27, while the model for the second year obtained a better R 2 of 0.34, a value arguably attributable to an increased volume of online activity. Moreover, the best model from the first academic year is partially generalizable to the second year, but explains a considerably lower variance (R 2 = 0.13). In addition to the quantitative analysis, a qualitative analysis of changes in student behaviors using comparative sociograms further supported conclusions that there were drastic changes in student behaviors observed as a function of the COVID-19 pandemic. • We evaluate students' behaviors before and during COVID-19 as a plug-in to Moodle. • A RNN combines global features with time series analysis to predict student grades. • Multiple sociograms are generated to observe interaction patterns. • A significant increase in online participation is observed during the pandemic. • The prediction model obtained an R 2 of 0.27 and 0.34 for the two academic years. [ABSTRACT FROM AUTHOR]