Talking Abortion (Mis)information with ChatGPT on TikTok

In this study, we tested users' perception of accuracy and engagement with TikTok videos in which ChatGPT responded to prompts about "at-home" abortion remedies. The chatbot's responses, though somewhat vague and confusing, nonetheless recommended consulting with health professionals before attempting an "at-home" abortion. We used ChatGPT to create two TikTok video variants - one where users can see ChatGPT explicitly typing back a response, and one where the text response is presented without any notion to the chatbot. We randomly exposed 100 participants to each variant and found that the group of participants unaware of ChatGPT's text synthetization was more inclined to believe the responses were misinformation. Under the same impression, TikTok itself attached misinformation warning labels ("Get the facts about abortion") to all videos after we collected our initial results. We then decided to test the videos again with another set of 50 participants and found that the labels did not affect the perceptions of abortion misinformation except in the case where ChatGPT explicitly responded to a prompt for a lyrical output. We also found that more than 60% of the participants expressed negative or hesitant opinions about chatbots as sources of credible health information

What are the barriers to primary prevention of type 2 diabetes in black and minority ethnic groups in the UK? A qualitative evidence synthesis.

Background: This review aimed to synthesise available qualitative evidence on barriers and facilitators to the implementation of community based lifestyle behaviour interventions to reduce the risk of diabetes in black and minority ethnic (BME) groups in the UK. Methods: A search of medical and social science databases was carried out and augmented by hand-searching of reference lists and contents of key journals. Qualitative evidence was synthesised thematically. Results: A total of 13 papers varying in design and of mainly good quality were included in the review. A limited number of intervention evaluations highlighted a lack of resources and communication between sites. A lack of understanding by providers of cultural and religious requirements, and issues relating to access to interventions for users was reported. Behaviour change was impeded by cultural and social norms, and resistance to change. There were variations in the way dietary change and physical activity was approached by different groups and contrasting practices between generations. Conclusions: Qualitative data provided insight into the ways that providers might improve or better design future interventions. Acknowledgement of the way that different groups approach lifestyle behaviours may assist acceptability of interventions

Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter

Warning users about misinformation on social media is not a simple usability task. Soft moderation has to balance between debunking falsehoods and avoiding moderation bias while preserving the social media consumption flow. Platforms thus employ minimally distinguishable warning tags with generic text under a suspected misinformation content. This approach resulted in an unfavorable outcome where the warnings "backfired" and users believed the misinformation more, not less. In response, we developed enhancements to the misinformation warnings where users are advised on the context of the information hazard and exposed to standard warning iconography. We ran an A/B evaluation with the Twitter's original warning tags in a 337 participant usability study. The majority of the participants preferred the enhancements as a nudge toward recognizing and avoiding misinformation. The enhanced warning tags were most favored by the politically left-leaning and to a lesser degree moderate participants, but they also appealed to roughly a third of the right-leaning participants. The education level was the only demographic factor shaping participants' preferences. We use our findings to propose user-tailored improvements in the soft moderation of misinformation on social media

Precise measurement of the left-right cross-section asymmetry in Z boson production by e+ e- collisions

We present a precise measurement of the left-right cross section asymmetry ($A_{LR}$) for $Z$ boson production by \ee collisions. The measurement was performed at a center-of-mass energy of 91.26 GeV with the SLD detector at the SLAC Linear Collider (SLC). The luminosity-weighted average polarization of the SLC electron beam was (63.0$\pm$1.1)%. Using a sample of 49,392 \z0 decays, we measure $A_{LR}$ to be 0.1628$\pm$0.0071(stat.)$\pm$0.0028(syst.) which determines the effective weak mixing angle to be \swein=0.2292\pm0.0009({\rm stat.})\pm0.0004({\rm syst.}).}Comment: 15 pages, no figure