# Text Analysis Methods for Misinformation-Related Research on Finnish Language Twitter
## 5 Discussion
Another avenue would be to consider possible machine learning methods to automate the labor-intensive phase of thematic analysis. However, compared to the word cloud analysis and the topic model of tweets, the combination of word count analysis and clustering does illustrate with more detail which political themes (for example) Twitter users combine with misinformation-related concepts. Nevertheless, word count analysis and clustering do not reveal which themes are linked with each other concerning misinformation, for example, which themes are linked with the Prime Minister. This would require coding the data with word stems and using manually coded data in combination with topic modeling. Regarding the contribution to the discovered main themes connected to the misinformation-related concepts in Finnish Twitter discussions, our results revealed that \"propaganda\" is the most utilized misinformation-related word. Conspicuously, this result was detectable with all three analysis methods. In addition, fake news (Fin. valeuutinen) and disinformation (Fin. disinformmaatio) were particularly represented misinformation-related words. Besides the keywords, the most used words in the Twitter data were generally neutral, i.e., their information value is not high without knowing the context. These are media-related words, such as news. Yet, they emphasize the communication style typical for microblogging on Twitter: tweeting, re-tweeting, and commenting on news and stories. Furthermore, other general words, such as politics, country, and world, are neutral words lacking context. Yet, they portray the landscape of the discussion content on Twitter. Furthermore, our results, particularly with the cluster analysis, revealed that politics and politicians are the main topics of discussion with regards to misinformation, disinformation, fake news, hoax, and propaganda in Finnish Twitter. Thus, this confirms the prior studies on fake news [29], as well as studies on false news spread on English Twitter [17]. In addition to the textual correlation, the study of disinformation has an emotional correlation. However, how and why disinformation flows--for example, regarding political polarization and immigration, particularly on social media such as Twitter--are questions that research regarding methods to analyze the text content of tweets in Twitter does not give answers. Therefore, we suggest further research of a descriptive or explanatory type with qualitative analysis methods on Twitter interactions and Twitter users. Patterns of how disinformation flows from one user to another user can be revealed, e.g., by analysis of information cascades on Twitter [44]. However, the rationale for why people disseminate misinformation inadvertently or disinformation intentionally on social media is discoverable from human motivation. A limitation of this study is that it only investigated the text content of tweets. Moreover, the data was limited to Twitter data in the Finnish language. Furthermore, the study was limited to the functionality of three methods for analyzing Twitter data. Naturally, there could also be other types of analysis methods for different types of Twitter data besides the text content of tweets that could be functional especially for tweets in the Finnish language or other languages.