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The consumption of news from social media is the new trend, still news channels are the authentic source to transmit relevant news to audiences. Social media has gradually left an impact on the audience but the news channels have upgraded and providing various news services online on social media websites. The present study aims to study the type of news videos uploaded by the top five Hindi TV news channels on their YouTube channels with an aim to see which type of videos spark interest for YouTube viewers.

By applying the techniques of content analysis, sentiment analysis and text mining the study aims to measure the average sentiments, top words and the trend of selected popular terms in the comments on uploaded news videos by the top five Hindi news channels over a period of one year.

Results of the study indicate that the news channels are uploading more news videos about crime and investigation, politics, health and protests while uploading fewer news videos covering travel, science and technology, and religion. While the viewers of the participating news channels are more interested in giving their thoughts or opinions in the form of comments on news videos concerning crime, politics, protests and health or that these videos inspire conversation on YouTube.

The findings might be of interest to content managers of news channels to understand the interest of their audience.

The study's distinctiveness resides in the approach utilised to collect data and analyse the results in order to better understand the online behaviour of news channel audiences.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2022-0007

Despite the actual prevalence of diverse types of multimedia information, research on video news is still in an early stage. Improving the accessibility of video news seems worth investigating, therefore, the purpose of this paper is to present a new combination mode of video news text clustering and selection. This method is useful for sorting out and classifying various types of news videos and media texts based on sentiment analysis.

A novel system is proposed, whereby video news are identified and categorized into good or bad ones via the authors' suggested Hidden Markov Model (HMM) and Support Vector Machine (SVM) hybrid learning method. Actually, an exploratory video news sentiment analysis case study, conducted on various news databases, has proven that the feature‐selection‐combining method, encompassing the Information Gain (IG), Mutual Information (MI) and CHI‐statistic (CHI), performs the best classification, which testifies and highlights the designed framework's value.

In fact, the system turns out to be applicable to several areas, especially video news, where annotation and personal perspectives affect the accuracy aspect.

The present work shows the way for further research pertaining to the personal attitudes and the application of different linguistic techniques during the classification.

The achieved results are so promising, encouraging and satisfactory, that they highlight the originality and efficiency of the authors' approach as an effective tool enabling to secure an easy access to video news and multi‐media texts.

When scientific information is unclear about the health benefits of foods, people choose to react in different ways. Using a posttest-only control group design, the authors tested how balanced and nonfactual information available on YouTube influences public perception of organic foods.

The authors randomly assigned participants (N = 640) from a southern US land grant university to watch one video: balanced news, nonfactual news, or control. All participants indicated changes in perception about organic foods immediately after the video. The authors analyzed the data using one-way and two-way ANOVA.

The nonfactual news video had the most influence on public perception of organic foods. Results confirmed that the effect of nonfactual information was more for individuals with preexisting beliefs consistent with the message communicated and individuals exposed to average to high levels of health and diet news.

The authors recommend regulatory changes in marketing strategies related to organic foods in the US that encourage balanced information about organic foods rather than promoting credence attributes of organic foods using persuasive information.

The authors findings suggest that, when scientific information about the health benefits of foods is unclear, communication activities should aim to increase healthy skepticism considering the audience's preexisting beliefs and frequency of health and diet news exposure.

Deepfakes are fabricated content created by replacing an original image or video with someone else. Deepfakes have recently become commonplace in politics, posing serious challenges to democratic integrity. The advancement of AI-enabled technology and machine learning has made creating synthetic videos relatively easy. This study explores the role of political brand hate and individual moral consciousness in influencing electorates' intention to share political deepfake content.

The study creates and uses a fictional deepfake video to test the proposed model. Data are collected from N = 310 respondents in India and tested using partial least square–structural equation modelling (PLS-SEM) with SmartPLS v3.

The findings support that ideological incompatibility with the political party leads to political brand hate, positively affecting the electorates' intention to share political deepfake videos. This effect is partially mediated by users' reduced intention to verify political deepfake videos. In addition, it is observed that individual moral consciousness positively moderates the effect of political brand hate on the intention to share political deepfake videos. Intention to share political deepfake videos thus becomes a motive to seek revenge on the hated party, an expression of an individual's ideological hate and a means to preserve one's moral self-concept and strengthen their ideologies and moral beliefs.

The study expands the growing discussion about disseminating political deepfake videos using the theoretical lens of the negative consumer-brand relationship. It validates the effect of political brand hate on irrational behavior that is intended to cause harm to the hated party. Further, it provides a novel perspective that individual moral consciousness may fuel the haters' desire to engage in anti-branding behavior. Political ideological incompatibility reflects ethical reasons for brand hate. Therefore, hate among individuals with high moral consciousness serves to preserve their moral self.

The proliferation of recording technologies has increased the prevalence of police intervention videos in news media. Although previous research has explored the influence of such coverage on public opinion, the mechanisms underlying this impact have received limited attention. This study investigates the role of information credibility in the assessment of police interventions portrayed in news media videos.

A total of 634 participants were shown a mock-up TV news report that included a description and a brief clip of a police use-of-force event. A survey was conducted before and after the presentation of the report.

Camera perspective, anchor tone, viewer gender and pre-existing perceived TV news credibility were found to influence how credible the mock-up news report was perceived. Participants who judged the news report as complete and credible tended to have a more favourable opinion of the police intervention. Perceived credibility also acted as a moderator in the relationship between video and receiver characteristics and the assessment of the police intervention.

These findings offer valuable insights for law enforcement agencies and their public affairs units to develop effective strategies for managing public opinion.

This research highlights how important perceived credibility is in influencing public opinion and how different factors such as video and receiver characteristics can impact credibility assessment.

Does the same news item on three different online news platforms, namely: newspapers, blogs and video news, impact each of perceived source credibility, likeability, content believability and attitude toward a message, differently? The paper aims to discuss these issues.

An experimental approach conducted among university students is adopted.

The psychometric properties of the instruments used are supported. Results showed that source credibility did not differ for the three platforms, indicating that respondents did not find one platform less credible than another. However, differences were observed on each of content believability, likeability and attitude toward the message. Online newspapers scored highest in all of these. Blogs came second in both content believability and likeability, while video news came second in attitude toward a message.

A number of limitations are noted. In particular, generalisability of findings to all youths in the country and beyond must be done with extreme caution.

The results suggest that the medium does change the message and online newspapers as a platform retain an advantage despite the arrival of alternative new media platforms, represented by blogs and video news. The latter emerges as the least effective indicating that respondents appear to prefer reading their news.

The paper uses an experimental approach and robust measures to compare news platforms across a number of elements in the communication process among a strong user segment.

Aiming to explore how audience consume and produce media events in the digital, distributed and social era we live in, the paper analyzes the viewing patterns of video news items during a media event (the week of Donald Trump's presidential visit to Israel, the first to a country outside the US), compared to a parallel comparable “ordinary” period (two weeks later, in which no inordinacy events occurred). The comparison focused on simultaneous activities of audiences engaged with the event, with either related (i.e. second screening) or unrelated (i.e. media multitasking).

The research is a diary study based on a dedicated mobile app in which respondents reported their news-related behavior during two periods: a media event period and comparable “ordinary” period.

Participants reported watching significantly more news video items in the first day of the media event week compared to the first day of the “ordinary” week. More than half of the viewing reports of the media event were not on TV. In the media event week, there were significantly higher percentages of viewing reports on smartphones/computers and significantly higher percentages of second-screening reports.

This is the first study that empirically explores the viewing patterns of video news items during a media event, compared to an “ordinary” period, focusing on media second screening of audiences engaged with the event. This comparison may reveal whether (1) media events still retain their centrality in a multi-screen era and (2) the role of the internet and online social media in the experience of media events.

Purpose – This chapter analyses social networks and discourses in relation to YouTube videos and user comments relating to the traumatic event of a school shooting.

Methodology/approach – First, general patterns in the YouTube responses are mapped. What was the overall structure of the flow of videos posted in response to a shooting? Second, social network aspects are discussed. Which systems of interrelated (re)actions emerge through the videos? Finally, a set of three videos representing key texts in the analysed discursive formation are further analysed as regards the written discourse of their comment threads.

Findings – Participants were organised in the form of relatively autonomous and isolated islands of meaning making, but one could still identify a core public engaging in the creation, maintenance and negotiation of the branching and relatively open-ended narratives that recount and try to make sense of what happened and why.

Implications – The main result is that, also in relation to largely dramatic and tragic events such as a school shooting, there are patterns to support the idea of an emerging new media landscape where audiences play an increasingly active role as co-producers of content and interpretations.

Originality of paper – The paper deals with comments as well as video content, and on analysing them from the joint perspectives of social network analysis and discursive network analysis. This means that results give knowledge about two things; how the YouTube audience(s) to videos about the Virginia Tech shooting is/are organised, and what topics are discussed in relation to the videos.

As user-generated content (UGC) is entering the news cycle alongside content captured by news professionals, it is important to detect misleading content as early as possible and avoid disseminating it. The purpose of this paper is to present an annotated dataset of 380 user-generated videos (UGVs), 200 debunked and 180 verified, along with 5,195 near-duplicate reposted versions of them, and a set of automatic verification experiments aimed to serve as a baseline for future comparisons.

The dataset was formed using a systematic process combining text search and near-duplicate video retrieval, followed by manual annotation using a set of journalism-inspired guidelines. Following the formation of the dataset, the automatic verification step was carried out using machine learning over a set of well-established features.

Analysis of the dataset shows distinctive patterns in the spread of verified vs debunked videos, and the application of state-of-the-art machine learning models shows that the dataset poses a particularly challenging problem to automatic methods.

Practical limitations constrained the current collection to three platforms: YouTube, Facebook and Twitter. Furthermore, there exists a wealth of information that can be drawn from the dataset analysis, which goes beyond the constraints of a single paper. Extension to other platforms and further analysis will be the object of subsequent research.

The dataset analysis indicates directions for future automatic video verification algorithms, and the dataset itself provides a challenging benchmark.

Having a carefully collected and labelled dataset of debunked and verified videos is an important resource both for developing effective disinformation-countering tools and for supporting media literacy activities.

Besides its importance as a unique benchmark for research in automatic verification, the analysis also allows a glimpse into the dissemination patterns of UGC, and possible telltale differences between fake and real content.