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In this blog post, Zia explores how "YouTube travelers practice a quiet kind of people-to-people diplomacy, drawing thousands of viewers with each video uploaded reshaping perceptions more effectively than any official campaign. What’s more, though, they also construct new illusions: realities filtered through framing and imagery where the line between representation and reality begins to blur as without a single policy statement, YouTubers have reframed the post-conflict country as a destination of beauty and normalcy." 

This study investigates the potential for integrating AI and journalistic networks to create real-time, priority-driven maps of infrastructure damage during natural disasters. Focusing on Hurricane Florence in 2018, we collected over a million tweets using the REST Twitter API and extracted 11,638 images for analysis. Tweets were categorized by source, including news organizations and citizen journalists. We applied the OpenAI CLIP unsupervised machine learning model for image classification, splitting the data into 80 % for training, 10 % for validation, and 10% for testing. The model achieved an average precision of 92 %, recall of 78 %, and an F1 score of 85 %. When compared to other models such as ViT and DeiT, which achieved F1 scores of 82.9 and 81.2, respectively, CLIP performed similarly but stood out due to its accessibility and zero-shot learning capabilities, making it ideal for rapid deployment in newsrooms and crisis scenarios. The framework's success was further demonstrated by cross-referencing model predictions with geotagged metadata and journalist sources, which linked damage locations with credible information. By leveraging this AI-based framework, journalists can significantly reduce the time needed to identify disaster-response targets, helping to focus relief and recovery efforts in real time. This approach enhances disaster data collection, analysis, and dissemination, ultimately saving lives and reducing harm by providing more efficient and accurate damage assessments. The study highlights how AI and journalistic networks can collaborate to improve crisis response efforts.

Big Tobacco and other stakeholders, such as vape shops and smaller e-cigarette manufacturers, have adapted traditional tobacco marketing techniques to digital platforms. Warning labels are essential for informing consumers about the potential harms of tobacco use, including e-cigarettes. However, in a rapidly changing digital landscape, social media platform policies often lag behind, leaving digital marketing largely unchecked. This has allowed Big Tobacco to modernize traditional cigarette marketing in the digital sphere with e-cigarettes, a phenomenon we term ‘remediated marketing’. Without adequate warning labels, exposure to tobacco promotion may increase e-cigarette use among youth, who engage with social media at particularly high rates. This article presents a rule-based classifier developed to detect warning labels in TikTok and YouTube videos by combining computer vision technology with rule-based classification. Our classifier achieved 97.33% accuracy in detecting posts with warning labels. However, only 2.32% of YouTube video frames (240 out of 10,344 frames) and 1.32% of TikTok video frames (61 out of 4639 frames) contained warning labels, suggesting that warning messages are infrequent across e-cigarette content on platforms popular among youth, including TikTok and YouTube. Among the detected warning labels, there was notable diversity in wording and length, indicating a lack of standardization. Additionally, within YouTube and TikTok video frames, 63.7% and 30.0% of the warnings appeared in the first five seconds of the videos, respectively. These results highlight the need for improved policies and standardized warning labels to better protect young adults from e-cigarette promotion on social media.