Exploring Audience Perceptions of, and Preferences for, Online News Videos

Koliska, Michael; Thurman, Neil; Stares, Sally; Kunert, Jessica

doi:10.1080/1461670x.2021.1927154

Cited by 11 publications

(11 citation statements)

References 60 publications

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“…Also building on Koliska et al's (2021) work, we propose a category of criteria that contains two antecedents of perception to reflect how readers' personal characteristics play a part in how they perceive and evaluate quantitative journalism.…”

Section: Discussionmentioning

confidence: 99%

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Exploring audience perceptions of, and preferences for, data-driven ‘quantitative’ journalism

2023

Self Cite

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Although data-driven ‘quantitative' journalism has increased in volume and visibility, little is known about how it is perceived and evaluated by audiences. This study helps fill this research gap by analysing the characteristics of quantitative journalism that a diverse group of 31 news consumers pay attention to and, within those characteristics, where their preferences might lie. In eight group interviews, participants read and discussed articles chosen to represent the diversity that exists in the forms and production of data-driven journalism. Our analysis reveals 28 perception criteria that we group into four major categories: antecedents of perception, emotional and cognitive impacts, article composition, and news and editorial values. Several criteria have not been used in prior research on the perception of quantitative journalism. Our criteria have obvious application in future research on how audiences perceive different types of quantitative journalism, including that produced with the help of automation. The criteria will be of interest too for researchers studying audience perceptions and evaluations of news in general. For journalists and others communicating with numbers, our findings indicate what audiences might want from data-driven journalism, including that it is constructive, concise, provides analysis, has a human angle, and includes visual elements.

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Section: Discussionmentioning

confidence: 99%

“…Two criteria that are frequently used to evaluate news texts, including from the audience perspective, are quality (e.g. Urban and Schweiger, 2014; Koliska et al, 2021) and credibility (e.g. Newhagen and Nass, 1989; Sundar, 1999).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring audience perceptions of, and preferences for, data-driven ‘quantitative’ journalism

2023

Self Cite

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“…Again, English-language channels are more professionalised than German-language channels in this regard: While English-language channels mainly use daily formats to pick up and comment on political issues, the German-language channels are characterised by slower upload frequencies at weekly intervals. Although YouTubers may benefit from a faster pace of communication in terms of user engagement and retention (Koliska et al, 2021), both types of upload regularity might endure, as it ultimately depends on the topics addressed and the way in which they are presented-similar to how slower forms of professional journalism have established alongside breaking news reporting (Mast et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Finally, all YouTube channels were examined regarding the frequency with which new videos are usually posted on these channels. Given that "upload frequency" can be a decisive factor in attracting and maintaining news audience attention (Koliska et al, 2021), this category represents another perspective on a channel's success beyond the specific content provided. The frequency with which new videos are usually uploaded to the channel was determined in two ways: If information regarding upload frequency was given in the channel info, this frequency was coded.…”

Section: Codebookmentioning

confidence: 99%

“…These activities are not limited to other social media platforms but additionally include collaborations with professional journalistic media (Lichtenstein et al, 2021). Cross-media activities are often pursued with some regularity, as audiences usually demand regular interaction and up-to-date information (Koliska et al, 2021). Meeting this demand is of considerable economic value, as it fuels web traffic, content sharing, and thereby reputation (Luo & Zhang, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Political Influencers on YouTube: Business Strategies and Content Characteristics

Fischer

Kolo

Mothes

2022

MaC

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Young media users increasingly engage with public affairs via social media such as YouTube, where content is increasingly produced by influencers who neither represent established professional news media nor political parties. Although the audience of these channels is already substantial in absolute terms and still growing enormously—making alternative influencers serious competitors to professional journalism—we still know little about their ways of attracting and monetizing audiences, the topics they emphasize, or the specific content they provide. To address this void, the present study examines political videos and their producers on YouTube in an explorative and comparative way for English- and German-speaking YouTube channels. We conducted a content analysis of the five most popular YouTube videos for each of the 20 most successful English- and German-speaking political influencers in 2020. Our analyses show that, although English YouTubers already appear to be more professionalized, similar patterns emerge in both language regions, particularly with regards to increasing efforts to manage microcelebrity status. In terms of content, two main types of political YouTube videos were identified: “partisan mockery” and “engaging education.” Results will be discussed in terms of their implications for political discourse, youth participation, and established journalistic media.

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Local feature‐based video captioning with multiple classifier and CARU‐attention

Im,

Chan

2024

IET Image Processing

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Video captioning aims to identify multiple objects and their behaviours in a video event and generate captions for the current scene. This task aims to generate a detailed description of the current video in real‐time using natural language, which requires deep learning to analyze and determine the relationships between interesting objects in the frame sequence. In practice, existing methods typically involve detecting objects in the frame sequence and then generating captions based on features extracted through object coverage locations. Therefore, the results of caption generation are highly dependent on the performance of object detection and identification. This work proposes an advanced video captioning approach that works in adaptively and effectively addresses the interdependence between event proposals and captions. Additionally, an attention‐based multimodel framework is introduced to capture the main context from the frame and sound in the video scene. Also, an intermediate model is presented to collect the hidden states captured from the input sequence, which performs to extract the main features and implicitly produce multiple event proposals. For caption prediction, the proposed method employs the CARU layer with attention consideration as the primary RNN layer for decoding. Experimental results showed that the proposed work achieves improvements compared to the baseline method and also better performance compared to other state‐of‐the‐art models on the ActivityNet dataset, presenting competitive results in the tasks of video captioning.

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Exploring Audience Perceptions of, and Preferences for, Online News Videos

Cited by 11 publications

References 60 publications

Exploring audience perceptions of, and preferences for, data-driven ‘quantitative’ journalism

Exploring audience perceptions of, and preferences for, data-driven ‘quantitative’ journalism

Political Influencers on YouTube: Business Strategies and Content Characteristics

Local feature‐based video captioning with multiple classifier and CARU‐attention

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