  Considerable advancements have been made to tackle the misrepresentation of
information derived from reference articles in the domains of fact-checking and
faithful summarization. However, an unaddressed aspect remains - the
identification of social media posts that manipulate information within
associated news articles. This task presents a significant challenge, primarily
due to the prevalence of personal opinions in such posts. We present a novel
task, identifying manipulation of news on social media, which aims to detect
manipulation in social media posts and identify manipulated or inserted
information. To study this task, we have proposed a data collection schema and
curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and
corresponding articles. Our analysis demonstrates that this task is highly
challenging, with large language models (LLMs) yielding unsatisfactory
performance. Additionally, we have developed a simple yet effective basic model
that outperforms LLMs significantly on the ManiTweet dataset. Finally, we have
conducted an exploratory analysis of human-written tweets, unveiling intriguing
connections between manipulation and the domain and factuality of news
articles, as well as revealing that manipulated sentences are more likely to
encapsulate the main story or consequences of a news outlet.
