This work adapts large language models to generate multilingual social media text that meets several objectives simultaneously: topic relevance, author style consistency, and reply validity. Leveraging existing online information behavior simulators, which currently only forecast activities but not content, our approach comprised of generalizable prompt formation and efficient evaluation to produce a believable, personalized, and responsive synthetic social network. According to some preliminary experiments, our multi-objective prompt formation and automatic evaluation/selection methods are able to yield a significant number of high-quality synthetic texts according to both standardized and trained metrics.

Disinformation on social media is impacting our personal life and society. The outbreak of the new coronavirus is the most recent example for which a wealth of disinformation provoked fear, hate, and even social panic. While there are emerging interests in studying how disinformation campaigns form, spread, and influence target audiences, developing disinformation campaign corpora is challenging given the high volume, fast evolution, and wide variation of messages associated with each campaign. Disinformation cannot always be captured by simple factchecking, which makes it even more challenging to validate and create ground truth. This paper presents our approach to develop a corpus for studying disinformation campaigns targeting the White Helmets of Syria. We bypass directly classifying a piece of information as disinformation or not. Instead, we label the narrative and stance of tweets and YouTube comments about White Helmets. Narratives is defined as a recurring statement that is used to express a point of view. Stance is a high-level point of view on a topic. We demonstrate that narrative and stance together can provide a dynamic method for real world users, e.g., intelligence analysts, to quickly identify and counter disinformation campaigns based on their knowledge at the time.