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Left- and Right-Leaning News Organizations Use Negative Emotional Content and Elicit User Engagement Similarly.

ABSTRACT: Negativity has historically dominated news content; however, little research has examined how news organizations use affect on social media, where content is generally positive. In the current project we ask a few questions: Do news organizations on Twitter use negative or positive language and which type of affect garners more engagement on social media? Does the political orientation of news organizations impact the affect expressed and engagement tweets receive on social media? The goal of this project is to examine these questions by investigating tweets of 24 left- and 20 right-leaning news organizations (140,358 tweets). Results indicated that negative affect was expressed more than positive affect. Additionally, negativity predicted engagement with news organizations' tweets, but positivity did not. Finally, there were no differences in affect between left- and right-leaning political orientations. Overall, it appears that for news organizations, negativity is more frequent and more impactful than positivity.

Supplementary information

The online version contains supplementary material available at 10.1007/s42761-021-00046-w.

SUBMITTER: Bellovary AK

PROVIDER: S-EPMC8364833 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in this context are necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention.The objectives of this paper are to discuss the utility of the joint modeling approach in the analysis of longitudinal engagement data in mHealth research and to illustrate the application of this approach using data from an mHealth intervention designed to support illness management among people with schizophrenia.Engagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to a naïve model that does not account for the relationship between dropout and engagement.The joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app 1 day more per week was associated with a 23% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model.Naïve longitudinal models that do not account for informative missingness in mHealth data may produce biased results. Joint models provide a way to model intensively collected engagement outcomes while simultaneously accounting for the relationship between engagement and missing data in mHealth intervention research.

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Left- and Right-Leaning News Organizations Use Negative Emotional Content and Elicit User Engagement Similarly.

Supplementary information

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