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Considerable advancements have been made to tackle the misrepresentation ofinformation derived from reference articles in the domains of fact-checking andfaithful summarization. However, an unaddressed aspect remains - theidentification of social media posts that manipulate information withinassociated news articles. This task presents a significant challenge, primarilydue to the prevalence of personal opinions in such posts. We present a noveltask, identifying manipulation of news on social media, which aims to detectmanipulation in social media posts and identify manipulated or insertedinformation. To study this task, we have proposed a data collection schema andcurated a dataset called ManiTweet, consisting of 3.6K pairs of tweets andcorresponding articles. Our analysis demonstrates that this task is highlychallenging, with large language models (LLMs) yielding unsatisfactoryperformance. Additionally, we have developed a simple yet effective basic modelthat outperforms LLMs significantly on the ManiTweet dataset. Finally, we haveconducted an exploratory analysis of human-written tweets, unveiling intriguingconnections between manipulation and the domain and factuality of newsarticles, as well as revealing that manipulated sentences are more likely toencapsulate the main story or consequences of a news outlet.

ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media