As the Internet media grows, fact-checking news articles online becomes increasingly difficult as it requires a vast amount of background knowledge. Recent studies proposed the concept of reason-checking, which focuses on analyzing the argumentative reasoning style of texts to identify low-quality news articles. While argument mining techniques are leveraged in automatic systems analyzing the quality of formally written texts such as essays, both its efficiency on news-editorial texts and its benefit in fake news detection are under-investigated. To this end, we analyze the performance of argument mining algorithms on fake news and explore how argumentation knowledge will help computational systems to identify fake news.