TY  - JOUR
A1  - Hagemann, Linus
A1  - Abramova, Olga
T1  - Sentiment, we-talk and engagement on social media
BT  - insights from Twitter data mining on the US presidential elections 2020
JF  - 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
U6  - https://doi.org/10.1108/INTR-12-2021-0885
SN  - 1066-2243
VL  - 33
IS  - 6
SP  - 2058
EP  - 2085
PB  - Emeral
CY  - Bingley
ER  - 
TY  - CHAP
A1  - Hagemann, Linus
A1  - Abramova, Olga
T1  - Crafting audience engagement in social media conversations
BT  - evidence from the U.S. 2020 presidential elections
T2  - Proceedings of the 55th Hawaii International Conference on System Sciences
N2  - Observing inconsistent results in prior studies, this paper applies the elaboration likelihood model to investigate the impact of affective and cognitive cues embedded in social media messages on audience engagement during a political event. Leveraging a rich dataset in the context of the 2020 U.S. presidential elections containing more than 3 million tweets, we found the prominence of both cue types. For the overall sample, positivity and sentiment are negatively related to engagement. In contrast, the post-hoc sub-sample analysis of tweets from famous users shows that emotionally charged content is more engaging. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with a vast number of followers. Prosocial orientation (“we-talk”) is consistently associated with more likes, comments, and retweets in the overall sample and sub-samples.
KW  - mediated conversation
KW  - big data
KW  - engagement
KW  - sentiment analysis
KW  - social media
Y1  - 2022
SN  - 978-0-9981331-5-7
SP  - 3222
EP  - 3231
PB  - HICSS Conference Office University of Hawaii at Manoa
CY  - Honolulu
ER  - 
TY  - CHAP
A1  - Abramova, Olga
A1  - Batzel, Katharina
A1  - Modesti, Daniela
T1  - Coping and regulatory responses on social media during health crisis
BT  - a large-scale analysis
T2  - Proceedings of the 55th Hawaii International Conference on System Sciences
N2  - During a crisis event, social media enables two-way communication and many-to-many information broadcasting, browsing others’ posts, publishing own content, and public commenting. These records can deliver valuable insights to approach problematic situations effectively. Our study explores how social media communication can be analyzed to understand the responses to health crises better. Results based on nearly 800 K tweets indicate that the coping and regulation foci framework holds good explanatory power, with four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Second, the inter-temporal analysis shows high volatility of topic proportions and a shift from self-centered to community-centered topics during the course of the event. The insights are beneficial for research on crisis management and practicians who are interested in large-scale monitoring of their audience for well-informed decision-making.
KW  - Digital-Enabled Human-Information Interaction
KW  - big data
KW  - data mining
KW  - health crisis
KW  - social media
Y1  - 2022
SN  - 978-0-9981331-5-7
PB  - HICSS Conference Office University of Hawaii at Manoa
CY  - Honolulu
ER  -