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Social media platform affordances allow users to interact with content and with each other in diverse ways. For example, on Twitter,¹users can like, reply, retweet, or quote another tweet. Though it’s clear that these different features allow various types of interactions, open questions remain about how these different affordances shape the conversations. We examine how two similar, but distinct conversational features on Twitter — specifically reply vs. quote — are used differently. Focusing on the polarized discourse around Robert Mueller’s congressional testimony in July 2019, we look at how these features are employed in conversations between politically aligned and opposed accounts. We use a mixed methods approach, employing grounded qualitative analysis to identify the different conversational and framing strategies salient in that discourse and then quantitatively analyzing how those techniques differed across the different features and political alignments. Our research (1) demonstrates that the quote feature is more often used to broadcast and reply is more often used to reframe the conversation; (2) identifies the different framing strategies that emerge through the use of these features when engaging with politically aligned vs. opposed accounts; (3) discusses how reply and quote features may be re-designed to reduce the adversarial tone of polarized conversations on Twitter-like platforms. 

Despite the central role that stories play in social movement-building, they are difficult to sustainably document for many reasons. To explore this challenge, this paper describes the design of a community-based conversational storytelling agent (CSA) to document digital stories of housing insecurity. Building on insights from an ongoing grassroots project, the Anti-Eviction Mapping Project, we share how a study initially focused on CSA-support opened an investigation of the role that artificial intelligence may play in housing justice movements. Drawing from 17 interviews with narrators of housing insecurity experiences and collectors of such stories, we find that collectors perceive opportunities to expand means of documentation with multimedia and multi-language support. Meanwhile, some narrators perceive potential for a CSA to offer therapeutic storytelling experiences and document otherwise unrecorded stories. Yet, CSA encounters also surface perils of machine bias, as well as reduced possibilities of human connections and relations. 

Well-intentioned users sometimes enable the spread of misinformation due to limited context about where the information originated and/or why it is spreading. Building upon recommendations based on prior research about tackling misinformation, we explore the potential to support media literacy through platform design. We develop and design an intervention consisting of a tweet trajectory-to illustrate how information reached a user-and contextual cues-to make credibility judgments about accounts that amplify, manufacture, produce, or situate in the vicinity of problematic content (AMPS). Using a research through design approach, we demonstrate how the proposed intervention can help discern credible actors, challenge blind faith amongst online friends, evaluate the cost of associating with online actors, and expose hidden agendas. Such facilitation of credibility assessment can encourage more responsible sharing of content. Through our findings, we argue for using trajectory-based designs to support informed information sharing, advocate for feature updates that nudge users with reflective cues, and promote platform-driven media literacy.