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Modern social media platforms like Twitch, YouTube, etc., embody an open space for content creation and consumption. However, an unintended consequence of such content democratization is the proliferation of toxicity and abuse that content creators get subjected to. Commercial and volunteer content moderators play an indispensable role in identifying bad actors and minimizing the scale and degree of harmful content. Moderation tasks are often laborious, complex, and even if semi-automated, they involve high-consequence human decisions that affect the safety and popular perception of the platforms. In this paper, through an interdisciplinary collaboration among researchers from social science, human-computer interaction, and visualization, we present a systematic understanding of how visual analytics can help in human-in-the-loop content moderation. We contribute a characterization of the data-driven problems and needs for proactive moderation and present a mapping between the needs and visual analytic tasks through a task abstraction framework. We discuss how the task abstraction framework can be used for transparent moderation, design interventions for moderators’ well-being, and ultimately, for creating futuristic human-machine interfaces for data-driven content moderation. 

Despite the widespread use of communicative charts as a medium for scientific communication, we lack a systematic understanding of how well the charts fulfill the goals of effective visual communication. Existing research mostly focuses on the means, i.e. the encoding principles, and not the end, i.e. the key takeaway of a chart. To address this gap, we start from the first principles and aim to answer the fundamental question: how can we describe the message of a scientific chart? We contribute a fact-evidence reasoning framework (FaEvR) by augmenting the conventional visualization pipeline with the stages of gathering and associating evidence for decoding the facts presented in a chart. We apply the resulting classification scheme of fact and evidence on a collection of 500 charts collected from publications in multiple science domains. We demonstrate the practical applications of FaEvR in calibrating task complexity and detecting barriers towards chart interpretability.