# Text Analysis Methods for Misinformation-Related Research on Finnish Language Twitter
## 5 Discussion  
The rapid spread of false information on Twitter has caused concern. Along with the social media applications, the various methods to study and analyze social media data, such as Twitter data, are evolving, too. Therefore, the functional analysis methods enable the investigation of discussions on misinformation and its related concepts, such as disinformation, fake news, and propaganda themes linked with those concepts in Twitter. By answering the research question: \"What are the functional methods to analyze Finnish Twitter data to discover out discussion themes that are linked with misinformation and concepts related to it?\", our article makes a two-fold contribution. First, it contributes to the applicability of three analysis methods on Finnish Twitter data. Secondly, it contributes to the discovered main themes that are connected to the misinformation-related concepts in Finnish Twitter discussions. Regarding the contribution of the applicability of three analysis methods to Twitter data, the article focused on describing and comparing methods to detect and analyze the text content of tweets in Twitter conversations in Finnish that includes the concepts of misinformation, disinformation, fake news, hoax, and propaganda, and the themes linked to them. Limiting the investigation to the text content of tweets was done to uncover the inherent challenges of text mining Finnish tweet contents and to understand what kind of insights could be drawn from only the text. The grammatical characteristics of the Finnish language, particularly regarding the word formation with endings to word stems, as well as word formation via derivation and compounding, challenges conventional data analysis methods as well as machine learning. Our results were obtained using three methods: (1) word cloud analysis, (2) topic model of tweets, and (3) word count analysis and clustering. Of these methods, the result of word cloud clustering is the most non-specific. Word cloud clustering does highlight the most utilized words visually and thus can easily present the relations in the manifestationsof various words. However, it regards variant forms of words as separate, therefore it does not lemmatize the same stem variants. Thus, for Finnish text, word cloud clustering is not the most ideal for producing 100% correct results. Rather, it is a good method to look for word stems for further analysis. Compared to word cloud analysis, the results of the second analysis method--the topic model of tweets--gives a more distinct view of the most utilized words in Twitter data. However, the topic model does not consider the stem variants or lemmatize the same stem variants. Therefore, the accuracy of the results with a language like Finnish is not precise. As with word cloud clustering, topic modeling is feasible for an overview of the themes of Twitter discussion and detecting the word stems of the most used words. The third method--word count analysis followed by clustering in a spreadsheet--does produce very detailed results for word count, followed by clustering the words to themes. Yet, manual stem variant detection and lemmatizing is a labour-intensive phase in the research. However, it does bring value to the research if accuracy and inclusiveness of as many words as possible are required. Another possible method could be that the main word stems are discovered with word cloud analysis, or better yet, with the topic model of tweets, and then the documents are coded automatically in Atlas.ti with the word stems. However, with the Finnish language, there is still the risk that irregularly delcinable words or conjugated verbs will not be included in the code list. In addition, natural language processing in Finnish is very limited. Finnish lemmatization could be carried out with Python with a very few libraries, such as Voikko and FINNPOS, yet their documentation is almost non-existent [43]. For further research, the above-mentioned coding in Atlas.ti could be suggested.