Content-based analysis of news video

Existing research on news video analysis mainly concentrates on structure analysis, semantic concept detection, annotation and search. However, little work has been contributed to news video people community analysis, which is helpful for users to understand the event. In this paper, we propose a novel approach to classify the people appearing in the news video into different communities. In our approach, the people appearing in the news video are first identified by associating their faces with names. The faces are detected from the video frames, and the names are obtained from the text. Then, the people belonging to the same organization are clustered. After that, the relationships between these organizations are determined using sentiment analysis. The sentiment words are diverse in each news story and contain both positive and negative ones. However, we have news title, which is the summary of the story and the sentiment of which is clear, to help us to mine the relationships between the organizations. At last, social networks are built to classify those people/organizations into different classes, and the people/organizations are ranked in each community according to their influence. The main contributions of the paper are two folds: 1) we propose a novel approach to present the news video event according to communities; 2) we propose to use the sentiment analysis and social network to classify the news people/organizations. The experimental results on the selected news topics demonstrate that the proposed approach is effective.

In this paper, we have developed a novel framework to enable more effective investigation of large-scale news video database via knowledge visualization. To relieve users from the burdensome exploration of well-known and uninteresting knowledge of news reports, a novel interestingness measurement for video news reports is presented to enable users to find news stories of interest at first glance and capture the relevant knowledge in large-scale video news databases efficiently. Our framework takes advantage of both automatic semantic video analysis and human intelligence by integrating with visualization techniques on semantic video retrieval systems. Our techniques on intelligent news video analysis and knowledge discovery have the capacity to enable more effective visualization and exploration of large-scale news video collections. In addition, news video visualization and exploration can provide valuable feedback to improve our techniques for intelligent news video analysis and knowledge discovery.

Public and commercial news follow distinct logics. We evaluate this duality in television news coverage on immigration. First, by means of a large-scale content analysis of Flemish television news ( N = 1630), we investigate whether immigration coverage diverges between both broadcasters. Results show that, despite an overall negativity bias and relative homogeneity between the broadcasters, commercial news contains slightly more sensational and tabloid characteristics than public news. The latter promotes a more balanced view of immigration. These differences are stable over time. Second, using cross-sectional and panel data, we assess whether a preference for public versus commercial news is associated with an attitudinal gap in anti-immigrant attitudes. Findings demonstrate that individuals who prefer commercial news are more negative towards immigrants. We suggest that differences in news content may explain this attitudinal gap. In light of the debate around ‘public value’ offered by public service media across Europe, we tentatively conclude that public broadcasters have the potential to foster tolerance and provide balanced information by prioritizing a normative view over a market logic. The linkage between news coverage and the gap in attitudes between commercial and public news viewers warrants closer investigation in the future.

In this paper we propose temporal and optimized video scene segmentation techniques. Temporal video scene segmentation methods aims to partition the video to elementary image sequences termed scenes. The main purpose of video scene segmentation is to extract objects from series of consecutive video frames. One important goal in video analysis is to group the shots, such that all shots in a single scene are related to the same subject. In this paper we make an analysis on four methods of video scene segmentation. The methods proposed for segmentation is Backward shot coherence(BSC), Best first model merging(BFMM) and Markov chain Monte Carlo (MCMC) and Hierarchical chain partitioning(HCP). Video scene segmentation is performed by considering both content coherence and temporally contextual dissimilarity. This task is formulated as a ratio function of content coherence within a scene and contextual dissimilarity of adjacent scenes. Finally we propose an optimized video scene segmentation which is a novel approach that is used to segment the video scene and thereby optimize them to produce efficiency. An effective and efficient hierarchical chain partitioning(HCP) approach is used to find the optimal scene segmentation. HCP gets competitive performance at low tolerance and better performance at high tolerance The HCP method approaches 100 % performance at tolerance = 1. The output segments can be used for further analysis and processing of the videos that is for indexing, storage, searching and retrieval of particular portion of video. It can be used in several applications in the fields of Image processing, video content analysis, Computer vision, Movies, Medical, Sports, and News. We have tested the proposed methods in Feature Films, News videos, TV Shows and high degree of accuracy has been obtained.

This paper reports on an investigation of the abilities of literates and adult nonreaders to recall and use information from a national network television news program. On a test of unaided recall of news stories the literates recalled 55% more stories than the nonreaders. On a multiple-choice test of information gain from the news program, the literates performed 63% better than the nonreaders. The samples were found to be virtually identical in their use of and opinions about television news. A control group of college students was given the information test without first seeing the news program and did no better than chance. The most important factor affecting recall was the length of time the story was on the air. Human interest stories were recalled much better by both groups than any other type of story. The level of performance among the adult nonreaders correlated highly with their reading levels.

Although entertainment is probably the most popular genre on television, news programmes are of particular significance in South Africa because of the social changes and racial conflict in the country. The main theory explored in the article is rooted in the assumption that news is a subjective, specific construction of reality, a product of the cultural context in which it operates and not really a mirror of reality. With regard to the contents of television news, the question of the national interest versus the public interest is also addressed. Results of in‐depth interviews conducted with opinion leaders in the news area in South Africa on their views with regard to the role (and the fulfilment of this role) of television news in nation building and the social transformation process in the country are analysed and discussed.

For most people in Britain, television news remains the most pervasive and most trusted source of information about the world. Television news is generally given greater credence by the public than either newspapers or radio; probably because it is perceived to be less partisan than the press, and because it offers the ‘evidence’ of pictures that isn’t available on radio. News broadcasts are also some of television’s most popular sources of programming: the evening news programmes from the BBC and ITN regularly feature in the high spots of the ratings. Unsurprisingly, discussion of television news has been controversial and contentious. Much of it has centred on accusations of ‘bias’, and that debate will be the subject of this chapter.

Technology-mediated group toxicity polarization is a major socio-technological issue of our time. For better large-scale monitoring of polarization among social media news content, we quantify the toxicity of news video comments using a Toxicity Polarization Score. For polarizing news videos, our premise is that the comments’ toxicity approximates either an “M” or “U” shaped distribution—that is, there is unevenly balanced toxicity among the comments. We evaluate our premises through a case study using a dataset of ~180,000 YouTube comments on ~3,700 real news videos from an international online news organization. Toward polarization-mitigating information systems, we build a predictive machine learning model to score the toxicity polarization of news content even when its comments are disabled or not available, as it is a current trend among news publishers to disable comments. Findings imply that the most engaging news content is also often the most polarizing, which we associate with increasing research on clickbait content and the detrimental effect of attention-based metrics on the health of online social media communities, especially news communities.

Characterizing top educational TikTok videos by dermatologists in response to “TikTok and dermatology: An opportunity for public health engagement”

This paper focus on TV news programs and design a content-based news video browsing and retrieval system, NVRS, which is convenient for users to fast browsing and retrieving news video by different categories such as political, finance, amusement, etc. Combining audiovisual features and caption text information, the system automatically segments a complete news program into separate news stories. NVRS supports keyword-based news story retrieval, category-based news story browsing and generates key-frame-based video abstract for each story. Experiments show that the method of story segmentation is effective and the retrieval is also efficient.

Drawing on a content analysis of television news and newspapers during the 2015 UK General Election along with semi-structured interviews with the heads and/or senior editors of news or politics from each broadcaster examined, we explore the intermedia agenda-setting influence of the national press during the campaign. Overall, we found similar policy-orientated agendas, with more stories emanating from right-wing newspapers and moments when front-page splashes dominated television news coverage. Many broadcasters were editorially comfortable with covering stories originating from newspapers if further context was supplied. Our findings do not point towards any deliberate political bias among broadcasters. We suggest instead that a range of structural constraints and professional routines encouraged broadcasters to feed off stories that were more likely to be supplied by right-leaning newspapers. Since news values are not politically neutral, we argue that if journalists or editors routinely rely on newspapers to help shape the political agenda it compromises their ability to make impartial judgements about news selection. Combining quantitative and qualitative analysis, we conclude, could help to better understand the editorial processes behind the selection of news and to more carefully interpret intermedia agenda-setting than large N studies can supply.

This thesis addresses contemporary debates in the sociology of science, the sociology of media and the public understanding of science by examining the UK television and print media coverage of three substantive scientific issues. The first case study follows the debate as to whether a Martian meteorite (ALH 84001) provided evidence of ancient bacterial life-forms that could prove primitive life had once existed on Mars. The second case study tracks the use of cell nucleus replacement to “clone” mammals. The third case study examines the scientific and political controversy over the existence, symptoms, causes and response to “Gulf War syndrome”. The empirical research employed methodologies developed at the Glasgow Media Group, providing a comparative analysis of the production, content and reception of news media coverage of the case studies. The methods included interviews with scientists, journalists, media professionals and interest groups, a two-year content analysis of press and television news and focus group interviews. The content analysis discovered that media coverage of the case studies was extensive, unevenly distributed over time and emphasised scientific and science-based controversy as news values. The production analysis highlighted the importance of the interaction between scientific institutions and the media. For example, science journalists regularly draw on scientific journals as credible source material. The scientific community actively maintained this situation through information subsidies as a way of generating science news. The reception analysis demonstrated that news media, especially television news, provided an important source of information for audience members. Audience members were active consumers of science news, based on their education, gender, age and personal experience of science. Motivation to consume science news was also a key factor for audience members, varying according to the scientific issue being covered by the media. Overall, this thesis has highlighted the contested interactions that construct and interpret news media coverage of science. By analysing these complex interactions, this thesis contributes to contemporary concerns about the public presentation of science.

It is widely assumed that British voters obtain a considerable amount of their political information from television and that television news plays an important part in shaping their political and economic perceptions. This study uses an experimental research design to assess the extent to which the political perceptions of a sample of 1125 UK voters tested during the 1997 general election campaign were affected by and party images presented in television news programmes. As well as being of intrinsic importance to debates about the effects of television news, this focus on positive and negative news has more than passing relevance for the kinds of campaigning that political parties adopt. The evidence reported in the paper shows that voters' views about the political parties can be swayed quite markedly by the content of television news. In particular, the analysis indicates that news images exert far more powerful effects on voters' perceptions than news.

In this study, we aimed to analyze scientific quality and content of female urethroplasty videos on YouTube. We searched YouTube using the "female urethroplasty", "female urethral stricture", and "urethroplasty" keywords on February 22, 2022. The quality and content of videos were analyzed using the Global Quality Score (GQS) and Female Urethroplasty-Specific Checklist Score (FUSCS) which was developed by our clinic. Video analysis was performed by two independent urologists. The relationship between the video characteristics and GQS and FUSCS was examined. A total of 38 videos were analyzed. Fourteen (36.8%) videos were uploaded by academic sources such as urology societies and universities/hospitals, while 24 (63.2%) videos were uploaded by urologists. The median GQS was 3 (range, 2-4) and the median FUSCS was 8 (range, 5-9) for all videos. The Cohen's kappa was 0.834 for GQS and 0.899 for FUSCS, indicating a high level of agreement between the observers. The median GQS was 4 (range, 4-5) and the median FUSCS was 9 (range, 8-10) for academic videos, indicating a statistically significantly higher scores than the urologists videos (p=.002 and p < .001, respectively). Academic videos on female urethroplasty on YouTube have adequate scientific quality and content for both patients and healthcare professionals. The number of videos by academic sources on female urethroplasty should be increased and individuals should be encouraged to search such videos on search engines.

Over the years, television news has tended to attract positive appraisal from viewing publics. Most viewers in developed democratic nations place trust in mainstream news broadcasters and, more significantly, believe that they learn a lot from televised news (Gunter, 1987; Towler, 2003; Pew Research, 2012). We might expect therefore that, if people are good judges of their own abilities to learn and have formed well-reasoned opinions about televised news based on their own extensive experiences, tests of learning from news broadcasts should reveal that a lot of information is retained from them. In addition, if we assume that news broadcasters, as trained professionals, know — as they claim to — what their audiences want and need, and know what they are doing in constructing digestible news, then news broadcasts should be produced in a way that will enhance viewers' understanding of current news events and issues. It may therefore come as a surprise that when viewers are tested for what they can remember from a television news programme they recently watched, most of its contents are lost to them. Furthermore, when specific news stories are recalled or recognised afterwards, viewers' understanding of specific details can be confused.KeywordsEpisodic MemoryNews StoryTelevision NewsNews EventNews BroadcastThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.