Background. Public discussions on social networks have trans-border and multilingual nature. This is especially true for conflictual discussions that reach global trending topics. Being part of the global public sphere, such discussions were expected by many observers to become horizontal, all-involving, and democratically efficient. But, with time, criticism towards the democratic quality of discussions in social media arose, with many works discovering the patterns of echo chambering in social networks. Even if so, there is still scarce knowledge on how affective hashtags work in terms of user clusterization, as well as on the differences between emotionally ‘positive’ and ‘negative’ hashtags. Objectives. We address this gap by analyzing the Twitter discussion on the Charlie Hebdo massacre of 2015. In this discussion, the Twittershpere has created #jesuischarlie and #jenesuispascharlie - two discussion clusters with, allegedly, opposite sentiments towards the journal’s ethics and freedom of speech. Research design. We were interested in whether echo chambers formed both on the hashtag level (based on language use) and within a language (based on user sentiment of French-speaking users). For data collection, we used vocabulary-based Twitter crawling. For data analysis, we employed network analytics, manual coding, web graph reconstruction, and automated sentiment analysis. Results. Our results show that #jesuischarlie and #jenesuispascharlie are alike in language distribution, with French and English being the dominant languages and the discussions remaining within the Euro-Atlantic zone. The language-based echo chambers formed in both cases. But if #jesuiuscharlie was a clear sentiment crossroads, #jenesuispascharlie was a negative echo chamber, thus allowing us to draw conclusions about multi-layer echo chambering.