Text style transfer is a challenging research task which modifies the linguistic style of a given text to meet pre-set objectives such as making the text simpler or more accessible. Though large language models have been found to give promising results, text rewriting to improve audience engagement of social media content is vastly unexplored. Our research investigates the performance of various prompting strategies in the task of rewriting Dutch news tweets in specific linguistic styles (formal, casual and factual). Apart from zero-shot and few-shot prompting variants, with and without personas, we also explore prompting with feedback on predicted engagement. We perform an extensive analysis of 18 different combinations of Large Language Models (GPT-3.5, GPT-4, Mistral-7B) and prompting strategies on three different metrics: ROUGE-L, semantic similarity and predicted engagement. We find that GPT-4 with feedback and persona prompting performs the best in terms of predicted engagement for all three language styles. Our results motivate further application of usage of prompting techniques to rewrite news headlines on Twitter to align with specific style guidelines.

Detecting stress from social media gives a non-intrusive and inexpensive alternative to traditional tools such as questionnaires or physiological sensors for monitoring mental state of individuals. This paper introduces a novel framework for finding reasons for stress from tweets, analyzing multiple categories for the first time. Three word-vector based methods are evaluated on collections of tweets about politics or airlines and are found to be more accurate than standard machine learning algorithms.