Breaking the News: First Impressions Matter on Online News

Online communities adopt various reputation schemes to measure content quality. This study analyzes the effect of a new reputation scheme that exposes one's offline social status, such as an education degree, within an online community. We study two Reddit communities that adopted this scheme, whereby posts include tags identifying education status referred to as flairs, and we examine how the “transferred” social status affects the interactions among the users. We computed propensity scores to test whether flairs give ad-hoc authority to the adopters while minimizing the effects of confounding variables such as topics of content. The results show that exposing academic degrees is likely to lead to higher audience votes as well as larger discussion size, compared to the users without the disclosed identities, in a community that covers peer-reviewed scientific articles. In another community with a focus on casual science topics, exposing mere academic degrees did not obtain such benefits. Still, the users with the highest degree (e.g., Ph.D. or M.D.) were likely to receive more feedback from the audience. These findings suggest that reputation schemes that link the offline and online worlds could induce halo effects on feedback behaviors differently depending upon the community culture. We discuss the implications of this research for the design of future reputation mechanisms.

Section: Propensity Score Modelingmentioning

confidence: 99%

“Trust Me, I Have a Ph.D.”: A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communities

Park

Kwak²,

Song

et al. 2020

“…These news websites need to create effective strategies to draw people attention to these contents. In this context, Reis et al (2015) investigate strategies used by online news organizations in designing their headlines. It was analysed the content of 69,907 headlines pro- duced by four major media companies during a minimum of eight consecutive months in 2014.…”

Section: Related Work Tension and Sentiment Analysis In Newsmentioning

confidence: 99%

Fusing Audio, Textual, and Visual Features for Sentiment Analysis of News Videos

Pereira

Pádua

Pereira

et al. 2021

This paper presents a novel approach to perform sentiment analysis of news videos, based on the fusion of audio, textual and visual clues extracted from their contents. The proposed approach aims at contributing to the semiodiscoursive study regarding the construction of the ethos (identity) of this media universe, which has become a central part of the modern-day lives of millions of people. To achieve this goal, we apply state-of-the-art computational methods for (1) automatic emotion recognition from facial expressions, (2) extraction of modulations in the participants' speeches and (3) sentiment analysis from the closed caption associated to the videos of interest. More specifically, we compute features, such as, visual intensities of recognized emotions, field sizes of participants, voicing probability, sound loudness, speech fundamental frequencies and the sentiment scores (polarities) from text sentences in the closed caption. Experimental results with a dataset containing 520 annotated news videos from three Brazilian and one American popular TV newscasts show that our approach achieves an accuracy of up to 84% in the sentiments (tension levels) classification task, thus demonstrating its high potential to be used by media analysts in several applications, especially, in the journalistic domain.

“…Accordingly, editors and journalists have adjusted the way they write headlines (Dick 2011). The characteristics of headlines in online news have been studied across the platforms, styles, sentiments, and news media (Kuiken et al 2017;Dos Reis et al 2015;Scacco and Muddiman 2019;Piotrkowicz et al 2017).…”

Section: Related Work News Media In the Era Of Social Mediamentioning

confidence: 99%

How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement

Park

Kwak

et al. 2021

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, the research community does not own a sufficient understanding of what kinds of editing strategies effectively promote audience engagement. In this study, we aim to fill the gap by analyzing media outlets' current practices using a data-driven approach. We first build a parallel corpus of original news articles and their corresponding tweets that eight media outlets shared. Then, we explore how those media edited tweets against original headlines and the effects of such changes. To estimate the effects of editing news headlines for social media sharing in audience engagement, we present a systematic analysis that incorporates a causal inference technique with deep learning; using propensity score matching, it allows for estimating potential (dis-)advantages of an editing style compared to counterfactual cases where a similar news article is shared with a different style. According to the analyses of various editing styles, we report common and differing effects of the styles across the outlets. To understand the effects of various editing styles, media outlets could apply our easy-to-use tool by themselves.