TY  - JOUR
A1  - Hagemann, Linus
A1  - Abramova, Olga
T1  - Sentiment, we-talk and engagement on social media
T2  - Internet research
N2  - 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.
KW  - social media
KW  - engagement
KW  - data mining
KW  - big data
Y1  - 2023
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/62537
SN  - 1066-2243
VL  - 33
IS  - 6
SP  - 2058
EP  - 2085
PB  - Emeral
CY  - Bingley
ER  -