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This paper aims to take, as a starting point, the contribution of audiovisual documentation to TV news programs, the impact of digitalisation in the organisation and design of audiovisual documentation's services is analysed.

Data, collected by a quantitative and qualitative research on: the use of audiovisual documentation in the news, documentation requests processed by journalists, and the study of the operation of documentation services of six TV stations, serve as a basis to analyse the factors that must be taken into account when it comes to designing query systems of digital audiovisual documentation, so that these systems meet the needs of journalists and can be used with satisfactory results by the users.

Audio‐visual documentation is one of the constituent elements of TV information on current events, as much for its quantitative presence (40 percent of the news) as for its qualitative contribution to news messages, as well as for its general use in all the news sections. Audiovisual documentation has a greater presence in important news, and can carry out informative, completive or illustrative functions. News programs use the audiovisual documentation that these same programs have generated, using it mainly as a purely visual documentation. In documentation services, the journalist asks mainly for people's images and, to a lesser extent, formal groups and the news. A second group of categories collects around 10 percent of requests: places, animal‐thing, natural phenomena, informal group; while the remaining categories (concept and work) have a marginal incidence. The analysis of documentation use in the news, as well as of the content of requests made by the journalists, offers important clues when it come to designing documentary information systems, specially regarding the analysis of audiovisual douments and databases' query, used directly by the end user.

Collected data regarding analogue TV are used to make forecasts about what should be documentation in digital TV.

The detailed analysis of the use of audiovisual documentation in the news, as well as of the requests made by the journalists to documentation services, constitutes an important guide when it comes to successfully designing the new digital systems of audiovisual documentation.

This paper discusses data-collection strategies that use digitized historical newspaper archives to study social conflicts and social movements from a global and historical perspective focusing on nationalist movements. I present an analysis of State-Seeking Nationalist Movements (SSNMs) dataset I, which includes news articles reporting on state-seeking activities throughout the world from 1804 to 2013 using the New York Times and the Guardian/Observer. In discussing this new source of data and its relative value, I explain the various benefits and challenges involved with using digitized historical newspaper archives for world-historical analysis of social movements. I also introduce strategies that can be used to detect and minimize some potential sources of bias. I demonstrate the utility of the strategies introduced in this paper by assessing the reliability of the SSNM dataset I and by comparing it to alternative datasets. The analysis presented in the paper also compares the labor-intensive manual data-coding strategies to automated approaches. In doing so, it explains why labor-intensive manual coding strategies will continue to be an invaluable tool for world-historical sociologists in a world of big data.

Social media (SM) platforms have become major sources for generating, sharing and gathering political and election news. Although there appears to be an assumption that reliance on SM for political news consumption will continue to gain in popularity, there are reasons to believe that many Americans are retreating from using SM for political news. The purpose of this study is to examine if Americans are reducing reliance on SM for political news and to analyze why retreat may be happening.

Using longitudinal panel data from Pew’s American Trends Panel study, the authors tracked 993 respondents from February of 2016 to November of 2019 to monitor their reliance on SM for political news leading up to the 2020 US presidential election.

The results of this study indicate that a sizeable percentage of people (about a third) are retreating from SM platforms for political news consumption and some are abandoning it altogether – people we refer to as new SM “nones.” The authors find that retreat from SM is associated with increased distrust of the information found on the platforms. Concerns about fake news, incivility on SM and information overload were unrelated to retreat from use of SM for political news consumption.

The findings of this study are novel and suggest that reliance on SM for political news by the public may have waxed, seen its zenith and may now be waning largely because of distrust in the information found on SM platforms.

Owing to the increased accessibility of internet and related technologies, more and more individuals across the globe now turn to social media for their daily dose of news rather than traditional news outlets. With the global nature of social media and hardly any checks in place on posting of content, exponential increase in spread of fake news is easy. Businesses propagate fake news to improve their economic standing and influencing consumers and demand, and individuals spread fake news for personal gains like popularity and life goals. The content of fake news is diverse in terms of topics, styles and media platforms, and fake news attempts to distort truth with diverse linguistic styles while simultaneously mocking true news. All these factors together make fake news detection an arduous task. This work tried to check the spread of disinformation on Twitter.

This study carries out fake news detection using user characteristics and tweet textual content as features. For categorizing user characteristics, this study uses the XGBoost algorithm. To classify the tweet text, this study uses various natural language processing techniques to pre-process the tweets and then apply a hybrid convolutional neural network–recurrent neural network (CNN-RNN) and state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) transformer.

This study uses a combination of machine learning and deep learning approaches for fake news detection, namely, XGBoost, hybrid CNN-RNN and BERT. The models have also been evaluated and compared with various baseline models to show that this approach effectively tackles this problem.

This study proposes a novel framework that exploits news content and social contexts to learn useful representations for predicting fake news. This model is based on a transformer architecture, which facilitates representation learning from fake news data and helps detect fake news easily. This study also carries out an investigative study on the relative importance of content and social context features for the task of detecting false news and whether absence of one of these categories of features hampers the effectiveness of the resultant system. This investigation can go a long way in aiding further research on the subject and for fake news detection in the presence of extremely noisy or unusable data.

The purpose of this study is to examine the knowledge gap hypothesis in the context of smartphone use for news to understand whether mobile news consumption could bridge or widen the knowledge gap between people of higher and lower socioeconomic status (SES).

The authors examine how smartphone news consumption is associated with the knowledge gap hypothesis by analyzing a survey dataset from Hong Kong. This study focuses specifically on a moderated mediation model in which the indirect effect of mobile news consumption on political knowledge via discussion network heterogeneity is contingent on level of education.

Smartphone use for news/information was positively associated with level of discussion network heterogeneity. The indirect effect of smartphone news use on political knowledge via discussion network heterogeneity was stronger for those with lower levels of education.

This study advances the understanding of the role of smartphone use in contributing to the functioning of deliberative democracy as this use enhances discussion network heterogeneity and general levels of political knowledge. Moreover, our study contributes to the literature on the knowledge gap by not only examining the relationship between smartphone use, discussion heterogeneity, and political knowledge but also taking into consideration individual levels of education.

The purpose of this study is to examine, in the context of online news use, the predictive values of two factors: perceived bias in traditional media and preference for partisan news.

This study used data collected as part of the Pew Internet and American Life Project between December 28, 2009, and January 19, 2010. The data were analyzed using linear regression analysis.

The findings provide evidence of the values of two potentially significant predictors of online news use: a perception of bias in traditional media and preference for partisan news. In addition, higher levels of political partisanship were shown to intensify the positive effect of perceived bias in traditional media on online news use in new media outlets, reinforcing the impact of preference for partisan news on participatory online news use.

Depending on individual decisions, the internet can either help to empower deliberative democracy (where diverse and different voices coexist) or lead to an extremely polarized society.

With the explosive growth of the internet as a news source, media scholars have explored the factors that encourage people to rely on the internet for news and information. Nevertheless, certain attributes of online news consumption originating from individual attitudes about and perceptions of the media environment remain underspecified. This research helps advance an understanding of the types of people who seek news online and how they use various sources.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.

The purpose of the study is to use web serer logs in analyzing the changes of user behavior in reading online news, in terms of desktop and mobile users. Advances in mobile technology and social media have paved the way for online news consumption to evolve. There is an absence of research into the changes of user behavior in terms of desktop versus mobile users, particularly by analyzing the server logs.

In this paper, the authors investigate the evolution of user behavior using logs from the Malaysian newspaper Berita Harian Online in April 2012 and April 2017. Web usage mining techniques were used for pre-processing the logs and identifying user sessions. A Markov model is used to analyze navigation flows, and association rule mining is used to analyze user behavior within sessions.

It was found that page accesses have increased tremendously, particularly from Android phones, and about half of the requests in 2017 are referred from Facebook. Navigation flow between the main page, articles and section pages has changed from 2012 to 2017; while most users started navigation with the main page in 2012, readers often started with an article in 2017. Based on association rules, National and Sports are the most frequent section pages in 2012 and 2017 for desktop and mobile. However, based on the lift and conviction, these two sections are not read together in the same session as frequently as might be expected. Other less popular items have higher probability of being read together in a session.

The localized data set is from Berita Harian Online; although unique to this particular newspaper, the findings and the methodology for investigating user behavior can be applied to other online news. On another note, the data set could be extended to be more than a month. Although initially data for the year 2012 was collected, unfortunately only the data for April 2012 is complete. Other months have missing days. Therefore, to make an impartial comparison for the evolution of user behavior in five years, the Web server logs for April 2017 were used.

The user behavior in 2012 and 2017 was compared using association rules and Markov flow. Different from existing studies analyzing online newspaper Web server logs, this paper uniquely investigates changes in user behavior as a result of mobile phones becoming a mainstream technology for accessing the Web.

The purpose of this paper is to reveal perceptions of news organization bias among people who use the internet.

Data for this study were drawn from the Pew Research Center June 2005 News Interest Index. Respondents were asked if news organizations were politically biased in their reporting. Another question asked respondents if news organizations had a liberal or conservative bias. The final question asked respondents to judge news organization bias on political and social issues.

In two of the three perceptions of internet user/non‐user ratings of ideological bias in news organizations, internet news users surveyed rate news organizations as more biased than non‐users. However, when asked to ascertain either liberal or conservative bias in news organizations, non‐internet news users were more likely to claim that news organizations were biased.

More valid measures of the dimension of liberal and conservative bias could help in analyzing the effect of this particular variable on the use of the internet for news. Also, there is the possibility of some confusion in identifying internet news sources.

Advertising revenue of traditional media could decline as news use shifts to internet sources, and customers of the traditional US news networks would continue to migrate to the internet.

As this new media technology has the potential to reach new markets throughout the world, consumers who perceive that traditional news media are ideologically biased may favor the new medium over more traditional sources of news.

This paper aims to investigate the mechanisms through which social media news consumption across different platforms leads to opinion polarization in society. To this end, the authors draw from cultivation theory to examine whether social media news consumption imparts a mainstreaming or resonance effect. Media consumption imparts a mainstreaming effect if frequent users, regardless of their social identity, develop homogenous attitudes about issues, whereas resonance is at play if there is a differing cultivation effect on various social groups depending on their relatability of life experiences.

The authors conduct the study in the developing context of India, using a population survey dataset from 2019. Regression-based mediation and moderation analyses were carried out to test the hypotheses.

The findings reveal that resonance is the most prominent mechanism through which social media news consumption cultivates opinion polarization, contrary to the mainstreaming effect imparted by television. Further, WhatsApp use was found to strengthen the polarizing effect of overall social media news consumption, while YouTube use weakened the cultivation of polarization.

The paper unearths how social media news consumption influences the opinion polarization of various social groups differently. The authors also find the differential effect of specific platform use. These findings have the potential to inform policymakers and developers about how to mitigate the detrimental effects of platform-based political persuasion.

This study offers significant contributions. First, the authors explain social media-induced polarization using the novel theoretical lens of cultivation. Second, the authors find that social media and television news consumption differ in their polarizing effects. Third, the authors find that while WhatsApp use amplifies the polarizing effect of social media news consumption, YouTube use weakens it.