Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action.

Increasing use of social media has resulted in many detrimental effects in youth. With very little control over multimodal content consumed on these platforms and the false narratives conveyed by these multimodal social media postings, such platforms often impact the mental well-being of the users. To reduce these negative effects of multimodal social media content, an important step is to understand creators' intent behind sharing content and to educate their social network of this intent. Towards this goal, we propose INTENT-O-METER, a perceived human intent prediction model for multimodal (image and text) social media posts. INTENT-O-METER models ideas from psychology and cognitive modeling literature, in addition to using the visual and textual features for an improved perceived intent prediction model. INTENT-O-METER leverages Theory of Reasoned Action (TRA) factoring in (i) the creator's attitude towards sharing a post, and (ii) the social norm or perception towards the multimodal post in determining the creator's intention. We also introduce INTENTGRAM, a dataset of 55K social media posts scraped from public Instagram profiles. We compare INTENT-O-METER with state-of-the-art intent prediction approaches on four perceived intent prediction datasets, Intentonomy, MDID, MET-Meme, and INTENTGRAM. We observe that leveraging TRA in addition to visual and textual features-as opposed to using only the latter-results in improved prediction accuracy by up to 7.5 % in Top-1 accuracy and 8 % in AUC on INTENTGRAM. In summary, we also develop a web browser application mimicking a popular social media platform and show users social media content overlaid with these intent labels. From our analysis, around 70 % users confirmed that tagging posts with intent labels helped them become more aware of the content consumed, and they would be open to experimenting with filtering content based on these labels. However, more extensive user evaluation is required to understand how adding such perceived intent labels mitigate the negative effects of social media.

AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19.

The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.