People are increasingly getting information and news from social media. On Twitter we are seeing the emergence of "tweetorials" -- long, explanatory Twitter threads written by experts. In this work we study tweetorials as a form of science writing. While scientists have begun to champion the importance of Twitter as a science communication medium, few have studied how people are successfully using this medium to communicate complex and nuanced ideas. To understand how tweetorials work, we curated a collection of 46 clear and engaging tweetorials from multiple domains. We analyzed these tweetorials for the writing techniques that they employ, and found that while tweetorials use many traditional science writing techniques, they also use more subjective language, actively build credibility, and incorporate media in unique ways. In addition, we report on a workshop we ran to aid science PhD students in writing tweetorials, and find that while providing common tweetorial techniques improves their writing, the students still struggle to balance their scientific sensibilities with the informal tone associated with tweetorials. We discuss the implications of using informal and subjective language in science communication, as well as how technology can support scientists in writing tweetorials.
https://doi.org/10.1145/3479566
Social interaction across multiple online platforms is a challenge for gender and sexual minorities (GSM) due to the stigmatization they face, which increases the complexity of their self-presentation decisions. These online interactions and identity disclosures can be more complicated for GSM in non-Western contexts due to consequentially different audiences and perceived affordances by the users, and limited baseline understanding of the conflation of these two with local norms and the opportunities they practically represent. Using focus group discussions and semi-structured interviews, we engaged with 61 Hijra individuals from Bangladesh, a severely stigmatized GSM from south Asia, to understand their overall online participation and disclosure behaviors through the lens of personal social media ecosystems. We find that along with platform audiences, affordances, and norms, participant skill/knowledge, and cultural influences also impact navigation through multiple platforms, resulting in differential benefits from privacy features. This impacts how Hijra perceive online spaces, and shape their self-presentation and disclosure behaviors over time. ContentWarning: This paper discusses graphic contents (e.g. rape and sexual harassment) related to Hijra.
https://doi.org/10.1145/3449231
Social media can be an especially effective source of feedback on open-ended work, such as product design. Unlike large businesses with entire teams dedicated to “social”, there is little understanding of how small business owners constrained both in personnel and resources can leverage the benefits of direct, informal communication channels afforded by social media. Through a series of design workshops and interviews with 26 small business owners at a local feminist makerspace, we uncover the unique challenges small business owners experience when seeking feedback on open-ended work via social media. We found participants carefully balanced large-scale access to diverse audiences with attempts to receive reliable feedback, and often targeted audiences narrowly to reinstate participants’ control and trust. While business owners idealized building authentic relationships with their social audience to create collectively, they shared the behind-the-scenes work needed to do so successfully such as navigating blurred personal and business identities, and self-regulation necessary to continuously stay engaged and not internalize discouraging feedback.
https://doi.org/10.1145/3449089
Online sources such as social media have become increasingly important for the proliferation of science news, and past research has shown that reading user-generated comments after an article (i.e. in typical online news and blog formats) can impact the way people perceive the article. However, recent studies have shown that people are likely to read the comments before the article itself, due to the affordances of platforms like Reddit which display them up-front. This leads to questions about how comments can affect people's expectations about an article, and how those expectations impact their interest in reading it at all. This paper presents two experimental studies to better understand how the presentation of comments on Reddit affects people's engagement with science news, testing potential mediators such as the expected difficulty and quality of the article. Study 1 provides experimental evidence that difficult comments can reduce people's interest in reading an associated article. Study 2 is a pre-registered follow-up that uncovers a similarity heuristic; the various qualities of a comment (difficulty, information quality, entertainment value) signal that the article will be of similar quality, ultimately affecting participants' interest in reading it. We conclude by discussing design implications for online science news communities.
https://doi.org/10.1145/3449106
Despite the increasingly important role played by image memes in online communication, the research community does not have a good understanding of the elements that can have an effect in making a meme go viral on social media. In this paper, we investigate what visual elements influence the chances that an image meme will go viral, across three dimensions: composition, subjects, and target audience. Drawing from research in vision, psychology, marketing, and neuroscience, we develop a codebook to characterize image memes, and use it to annotate a set of 100 image memes collected from 4chan's Politically Incorrect Board (/pol/). We find that image memes that use a close-up scale and contain characters are more likely to go viral, and so are those including positive or negative emotions. Overall, our analysis sheds light on what indicators characterize viral visual content online, and set the basis for developing better techniques to create or moderate content that is more likely to catch the viewer's attention.
https://doi.org/10.1145/3449155
In contrast to existing, static audience controls that map poorly onto users’ ideal audiences on social networking sites, dynamic audience selection (DAS) controls can make intelligent inferences to help users’ select their ideal audience given context and content. But does this potential utility outweigh its potential intrusiveness? We surveyed 250 participants to identify model users’ ideal versus their chosen audiences with static controls and found a significant misalignment, suggesting that DAS might provide utility. We then designed a sensitizing prototype that allowed users to select audiences based on personal attributes, content, or context constraints. We evaluated DAS vis-a-vis Facebook’s existing audience selection controls through a counterbalanced summative evaluation. We found that DAS’s expressiveness, customizability, and scalability made participants feel more confident about the content they shared on Facebook. However, low transparency, distrust in algorithmic inferences, and the emergence of privacy-violating side channels made participants find the prototype unreliable or intrusive. We discuss factors that affected this trade-off between DAS’s utility and intrusiveness and synthesize design implications for future audience selection tools.
https://doi.org/10.1145/3476083
The rise of social media has changed the nature of the fashion industry. Influence is no longer concentrated in the hands of an elite few: social networks have distributed power across a broader set of tastemakers. To understand this new landscape of influence, we created FITNet --- a network of 10,000 fashion-related Twitter accounts that heavily influence the larger Twitter fashion graph. To construct FITNet, we trained a content-based classifier to predict which accounts are related to fashion. Leveraging this classifier, we estimated the size of Twitter's fashion subgraph, snowball sampled more than 300k fashion-related accounts based on following relationships, and identified the top 10,000 influencers, or FITNet, in the resulting subgraph. We asked 55 fashion undergraduates to validate and further categorize the accounts in FITNet. These categorizations allow us to analyze interactions between influential people, brands, retailers, and media in fashion.
https://doi.org/10.1145/3449227
How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase both the inequality and unpredictability of its success, but has only been shown in the context of music listening. To further examine the effect of social influence on media popularity, we extend this research to the context of algorithmically-generated images by re-adapting Salganik et al's Music Lab experiment. On a digital platform where participants discover and curate AI-generated hybrid animals, we randomly assign both the knowledge of other participants' behavior and the visual presentation of the information. We successfully replicate the Music Lab's findings in the context of images, whereby social influence leads to an unpredictable winner-take-all market. However, we also find that social influence can lead to the emergence of local cultural trends that diverge from the status quo and are ultimately more diverse. We discuss the implications of these results for platform designers and animal conservation efforts.
https://doi.org/10.1145/3479553