To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (non-negative matrix inter-joint factorization; topic alignment) and qualitative (thematic analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

Misinformation often has a continuing effect on people's reasoning despite clear correction. One factor assumed to affect post-correction reliance on misinformation is worldview-driven motivated reasoning. For example, a recent study with an Australian undergraduate sample found that when politically situated misinformation was retracted, political partisanship influenced the effectiveness of the retraction. This worldview effect was asymmetrical, that is, particularly pronounced in politically conservative participants. However, the evidence regarding such worldview effects (and their symmetry) has been inconsistent. Thus, the present study aimed to extend previous findings by examining a sample of 429 pre-screened US participants supporting either the Democratic or Republican Party. Participants received misinformation suggesting that politicians of either party were more likely to commit embezzlement; this was or was not subsequently retracted, and participants' inferential reasoning was measured. While political worldview (i.e. partisanship) influenced the extent to which participants relied on the misinformation overall, retractions were equally effective across all conditions. There was no impact of political worldview on retraction effectiveness, let alone evidence of a backfire effect, and thus we did not replicate the asymmetry observed in the Australian-based study. This pattern emerged despite some evidence that Republicans showed a stronger emotional response than Democrats to worldview-incongruent misinformation. This article is part of the theme issue ‘The political brain: neurocognitive and computational mechanisms’.

To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content, without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (Non-Negative Matrix inter-joint Factorization; Topic Alignment) and qualitative (Thematic Analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.