Comment sections below news articles are public fora in which potentially everyone can engage in equal and fair discussions on political and social issues. Yet, empirical studies have reported that many comment sections are spaces of selective participation, discrimination, and verbal abuse. The current study complements these findings by analyzing gender-related differences in participation and incivility. It uses a sample of 303,342 user comments from 14 German news media Facebook pages. We compare participation rates of female and male users as well as associations between the users’ gender, the incivility of their comments, and the incivility of the adjacent replies. To determine the incivility of the comments, we developed a Supervised Machine Learning Model (classifier) using pre-trained word embeddings and word// frequency features. The findings show that, overall, women participate less than men. Comments written by female authors are more civil than comments written by male authors. Women’s comments do not receive more uncivil replies than men’s comments and women are not punished disproportionately for communicating uncivilly. These findings contribute to the discourse on gender-related differences in online comment sections and provide insights into the dynamics of online discussions.

The chapter gives an overview over the research on user comments in comments sections below online news items. It focuses on content analytic studies. It reviews the most important theoretical frameworks, research designs, and main constructs applied in investigations of user comments.

The inclusion of narratives in evidence-based patient information is a heavily discussed topic in literature. Narratives elicit intense engagement and emotional insights, but may also cause unintended persuasion effects. There is mixed evidence that adding narratives to non-narrative factual patient information is valuable for patients. In addition, providing patients with narratives about the outcome of treatments has been found to bias treatment decisions, in line with the direction implied by the narratives. This may counteract informed decision making of patients. However, narratives about the process of a treatment and personal experiences with treatments may support the well-being of patients in tertiary prevention. In order to investigate patients' views on narratives and their function in patient information, we conducted 26 semi-structured qualitative interviews with pulmonary embolism patients. Answers were coded using thematic analysis. Results show that patients are especially interested in experience and process narratives when combined with evidence-based patient information. We identified four main functions of experience and process narratives that patients ascribe to these narratives: (1) motivating self-reflection and reflection on the recovery process, (2) reducing the feeling of loneliness, (3) reducing emotional distress, and (4) inspiring mindful mastery.

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The variable ‘number of reply comments’ is an indicator of interactivity in online discussions. The number of reply comments is a simple measure of how much response a user comment has received. It is applicable if platforms that host comment sections offer the technical possibility to users to respond directly to existing user comments. The reply comments (also called ‘sub-level comments’ or ‘child comments’) then usually appear indented below the existing user comment they refer to (also called ‘top-level comment’ or ‘parent comment’). The measure does not provide information about the quality of the interaction between the commenters. It neither covers interactivity that occurs “outside” of comment threads below top-level comments, i.e. commenters responding in new top-level comments instead of sub-level comments. Field of application/theoretical foundation: Normative approaches to discourse ethics (e.g. Habermas, 1992) evaluate interactivity as a prerequisite for high-quality discourses. Example studies: Medium Measure Unit of analysis Studies Online; online discussions below news posts Number of reply comments (sub-level comments) to a top-level comment Individual user comments Naab & Küchler (work in progress)   Info about variables Variable name/definition: Anzahl der Antwortkommentare auf einen Nutzerkommentar Operationalization/coding instructions: Manuell: Zählen Sie die Kind-Kommentare (Antwortkommentare, Second-Level-Kommentare), die zu einem Eltern-Kommentar (Top-Level-Kommentar) verfasst wurden und tragen die Anzahl ein. Automatisiert: Sofern ein Datensatz alle Nutzerkommentare eines Kommentarthreads, Informationen über das Eltern- bzw. Kind-Level jedes Kommentars sowie eine Zuordnung aller Kind-Kommentare zu einem Eltern-Kommentar enthält, ist es möglich, die Anzahl der Kind-Kommentare für jeden Eltern-Kommentar per Auswertungssoftware zu aggregieren. Level of analysis: Kommentarthread (Eltern-Kommentar + alle zugehörigen Kind-Kommentare/Antwortkommentare)   References Naab, T.K. & Küchler, C. (work in progress). Unveröffentlichtes Codebuch aus dem DFG-Projekt „Gegenseitige Sanktionierung unter NutzerInnen von Kommentarbereichen auf Nachrichtenwebseiten und auf Facebook“. Augsburg. Habermas, J. (1992). Faktizität und Geltung: Beiträge zur Diskurstheorie des Rechts und des demokratischen Rechtsstaates. Suhrkamp.