CONCLUSIONS AND DISCUSSIONS
5.4. Limitations and Future Research
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Third, this research extended the application of intermedia contents. Through using the semantic way, this research investigated possibilities of using intermedia contents to measure partisan slant. Past research (e.g., Gentzkow & Shapiro, 2010) used different media contents as an index or parameter to measure news media’s partisan leaning, while this study explored to use Facebook contents as an index to measure news media’s partisan leaning. Additionally, this research explored different framing and salient issues between news media and social media on the text level.
Forth, as for the data cleaning procedure, this research enriched the applications of the computer assisted softwares to process data analysis. This rearch processed data cleaning before the analysis through R programming language. The word segmentation was conducted by JiebaR package (Qin & Wu, 2016), and the co-word matrix was processed by Quanteda package (Benoit et al., 2019). It contributed to the practices of data cleaning on text mining and text analysis, through application of R programming langue and the packages for future studies.
The utilizing of JiebaR package on Traditional Chinese natural language processing would be a valuable application as it was first launched for Simplified Chinese language as a word segmentation tool.
5.4. Limitations and Future Research
First, this research considered the news contents as the key factor leading slant. However, as mentioned in the literature review, several factors include economics concerns such as media marketplace or media reputation, government power, or public attitudes might affect media slant. For example, one of them used media coverage composing an ideodynamic model as
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prediction of public attitudes (Watts, Domke, Shah & Fan, 1999). Although it could make our study focus more on the content implications on partisan slant, past studies treated these factors mentioned above crucial and might influence or be influenced by media slant. Future study could consider to include additional factors to investigate media slant.
Second, this study would firstly use social media as the parameter to identify the partisan leaning of online news media. As the news articles used third-person narrative for reporting, the social media contents often use more interactive narrative ways for the engagement. The different ways of narratives on media platforms might cause different words of use. The limitation and gap might occur in two media platforms due to their different features and narrative ways of contents. Despite the fact that previous study suggested a mutual way or a dynamic transmitting pattern between social media and news media information flows (Wang
& Guo, 2018; Neuman et al, 2014), suggesting that the possibility of intermedia similar issue cycle, it was still worth future investigations on semantic analysis of different media types that have different words of use and narrive features, and to further evaluate the validity that considers using different types of media contents as the index.
Third, this research used semantic network analysis, extracting salient concepts on an environemtnal referendum issue, in order to map the framing of different types of media (i.e.
social media and news media), and two types of news media (i.e. traditional and digital native media). However, it would be limited to probe into further insights in semantic networks as they were made up of words. First of all, it was not way to observe the relationship between 100 words while comparing networks. Second, many words were highly co-occurred in documents. Third, selecting a small number of words as the node size were likely to limit the
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lexicon or word base representing the communicators’ (i.e. DPP/KMT actors, environmental groups, news media) speech or position regarding the issue. Forth, to conduct semantic network analysis, the procedure of word segmentation decontextualized the story of news articles or Facebook posts, which was not an easy way to read the thread of the story, or the position of the issue, and it might cause the misinterpretation of the story when this research would like to recontextualize and contruct the story on the basis of the semantic network.
As for the methodological concerns through QAP analysis, some scholars compared their semantic networks through angenda issue networks, which were made up of “themes” (NAS model) rather than “words” (Guo & Vargo, 2015; Vargo, Guo, McCombs & Shaw, 2014; Guo, 2012). Each network for comparison remain same number of nodes for the QAP analysis. In comparison, this research processed the word segmentation that every document (post or article) pocessessed an unequal number of words. Also, this research selected 100 words to build up the matix to keep the all networks with same node number. Due to the limitations that the networks were made up by words, there were difficulties in observing relationship between 100 words, high co-occurrence in documents, and concerns of the lexicon or word base. Thus, the future research can include thematic analysis, (e.g., taking reference on NAS model) to further investigate on network relationship observations in QAP correlation analysis.
Forth, this study used semantic network analysis to explore the framing and the partisan slant. It would be a limit only using semantic network analysis although it focuses primarily on content analysis. However, as previously mentioned, several factors would cause media slant. Several past studies used economic models to measure media slant (Gentzkow & Shapiro,
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2010; Lichter, 2017; Gentzkow et al., 2015). Future studies could explore or compare more ways to measure media slant or bias.
Finally, this study only used post and article trends as the observations of intermedia agenda setting between social media and news media, in order to support the dynamic and repciprocal transmitting pattern across media platform, giving the basis of using social media contents as partisan slant parameter of news media. In the past studies that proved the dynamic intermedia agenda setting patterns, scholars not only observed time series of content frequency, they also utilized framework of Granger causality (Granger, 1969), which identified the peridotic cycle and trend between two time-series of media contents (Neuman et al., 2014; Jang et al., 2019). Future studies were suggested extend the intermedia partisan slant research to the time order causality examinatation.
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