Thursday 22 June 2023

Recommendations for QDAS developers from 'Noteworthy disparities with four CAQDAS tools- explorations in organising live Twitter data', forthcoming

Dr Corrie Uys, Dr Pat Harpur and I are working on a manuscript that explores the research implications of differences in Qualitative data analysis software (QDAS) packages’ support for live Twitter data imports. This paper's software comparison contrasts the four prominent QDAS tools that support such imports, namely ATLAS.ti™, NVivo™, MAXQDA™ and QDA Miner™. We discuss key discrepancies in their use during the organisational phase of qualitative research and address related methodological issues.

Outside the paper's scope, our software comparison also uncovered several suggestions that developers of these QDAS tools might follow to improve the user experience for Twitter researchers:

1 Make tweets easier to sort & link them to their original context 

QDAS typically present a myriad of isolated tweets in one spreadsheet document that seems to divorce tweets from their conversational context. Researchers would benefit from being able to order and sort tweets as data. QDAS should also provide the option to quickly link to the original tweet in Twitter. Only NVivo made it relatively efficient to see the original context of a tweet in a Twitter discussion.

2 Provide more extensive support for modes and Twitter affordances

Linking to the original context with Twitter is particularly important where audio, emoji, font, image, and video modes and Twitter affordances for hashtagging and @mentions disappear. These may not be imported into QDAS spreadsheets as QDAS tools differ widely in the data they extract for Twitter affordances and modes. 

3 Support conversational analysis

Research into Twitter conversations was poorly supported by all four QDAS tools. Each presented a myriad of isolated tweets, with no way to display the original conversational thread. QDAS and Twitter should work together for providing qualitative researchers with ready access to Twitter exchanges. The added benefits of API2 functionality (such as conversation tracking) seem MIA in QDAS. Such integration would seem a useful step for promoting wider research into healthy conversations that Twitter described in 2018 as an important business priority.

4 Provide examples for live Twitter data analysis

QDAS companies that provide Twitter import functionality should provide resources that address not only how to extract data, but also examples of how their software is used in analysing microblogging data. While Twitter is actively encouraging and training academic researchers to transform raw JSON into CSV files for research purposes, QDAS companies seem to provide scant examples for live Twitter data analysis. The online resources they provide could be improved by adding examples. For example, we look forward to seeing how QDAS are used in analysing Twitter conversation threads.

5 Spotlight the black box of Twitter data organisation

QDAS developers could make the ‘black box’ of Twitter data organisation visible by showing a model of the data undergirding the tweets, and also the spreadsheet's data excludes. Researchers could benefit from such an overview for the great deal of Twitter fields that are missing.

6 Missing in extraction

Another black box concerns the process of data extraction from Twitter. While the functionality of running live imports for select criteria is efficient, more information could be shared regarding the context of the extraction. For example, what are the internal and external limits on the maximum number of tweets a QDAS can import.

Do let us know what you think of these suggestions by submitting a comment below, or contacting me.

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