  Social media platforms have grown into an important medium to spread
information about an event published by the traditional media, such as news
articles. Grouping such diverse sources of information that discuss the same
topic in varied perspectives provide new insights. But the gap in word usage
between informal social media content such as tweets and diligently written
content (e.g. news articles) make such assembling difficult. In this paper, we
propose a transformation framework to bridge the word usage gap between tweets
and online news articles across languages by leveraging their word embeddings.
Using our framework, word embeddings extracted from tweets and news articles
are aligned closer to each other across languages, thus facilitating the
identification of similarity between news articles and tweets. Experimental
results show a notable improvement over baselines for monolingual tweets and
news articles comparison, while new findings are reported for cross-lingual
comparison.
