Virality has been widely studied in many research projects like Wang and Liu 2016, Hashtags and information virality in networked social movement: Examining hashtag co-occurrence patterns, show that using co-occurrence graphs (hashtags that appear in the same tweet) they can find what hashtags are more likely to become viral. In our work we are more interested in the type of language that is used when using what hashtag vs, the other hashtags. Also Xiong et al (2019) Hashtag activism and message frames among social movement organization: Semantic network analysis and thematic analysis of Twitter during the [#MeToo](/relevant/new/MeToo) movement, where the authors used the co-occurrence of words to identify the network structure of the messages. Their work also mentions that bottom up processes like Unbranded Hashtags or Events are more likely to be ‘viralized’ in terms of how informative the content is. They define shortness and informative as attributes that go viral for these kinds of events, while Top-down hashtags should be promoted actively by the interested groups.