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Purpose
Given inconsistent results in prior studies, this paper applies the dual process theory to investigate what social media messages yield audience engagement during a political event. It tests how affective cues (emotional valence, intensity and collective self-representation) and cognitive cues (insight, causation, certainty and discrepancy) contribute to public engagement.
Design/methodology/approach
The authors created a dataset of more than three million tweets during the 2020 United States (US) presidential elections. Affective and cognitive cues were assessed via sentiment analysis. The hypotheses were tested in negative binomial regressions. The authors also scrutinized a subsample of far-famed Twitter users. The final dataset, scraping code, preprocessing and analysis are available in an open repository.
Findings
The authors found the prominence of both affective and cognitive cues. For the overall sample, negativity bias was registered, and the tweet’s emotionality was negatively related to engagement. In contrast, in the sub-sample of tweets from famous users, emotionally charged content produced higher engagement. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with many followers. Collective self-representation (“we-talk”) is consistently associated with more likes, comments and retweets in the overall sample and subsamples.
Originality/value
The authors expand the dominating one-sided perspective to social media message processing focused on the peripheral route and hence affective cues. Leaning on the dual process theory, the authors shed light on the effectiveness of both affective (peripheral route) and cognitive (central route) cues on information appeal and dissemination on Twitter during a political event. The popularity of the tweet’s author moderates these relationships.
Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.