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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.

This is a literature survey paper. The purpose of this paper is to focus on the latest developments in textual analysis on China’s financial markets, highlighting its differences from existing works in the US markets.

The authors review the literature and carry out an experiment of sentiment analysis based on a small sample of Chinese news articles.

Based on the experiment of sentiment analysis, there is limited evidence on the association between sentiment and other contemporaneous or future returns.

The supply of financial textual information has grown exponentially in the past decades. Technological advancements in recent years make the programming-based analysis an effective tool to digest such information. The authors highlight the use of credible textual information and discuss directions of research in this important field.

The dynamics of platforms, particularly the eventual need for renewal, are too often neglected. This chapter adopts a four-stage model – Birth, Expansion, Leadership, and Self-Renewal – to analyze the requirements at each stage of the platform lifecycle in terms of its dependence on the high-level dynamic capability categories of sensing, seizing, and transforming. The requirements evolve from a heavy emphasis on generative sensing and planning-stage seizing in the birth phase, through greater emphasis on “seizing” activities and minor transformations as the platform, ideally, grows and stabilizes. When platform renewal is called for, the emphasis returns to sensing future possibilities and generating new ideas for a platform and business model, developing them alongside the existing business, and eventually undertaking a major transformation to restart the platform lifecycle. An awareness of these lifecycle changes can help managers adopt a longer-term perspective on the competitive requirements of their platform-based business.

The journalism profession has radically changed due to the digitisation and the development of new media. As content is moving online, rapidly evolving Internet technologies have affected basic journalistic work processes. In this context, changes in technology as well as audience engagement have greatly expanded the skills required to be a professional journalist nowadays. A number of studies have shown that search engines constitute an important source of the traffic to online news outlets around the world, identifying the significance of top rankings in search results. Concurrently, in the digital age, the interest in monitoring online activities as well as the significance of studying the traffic data has intensified. This chapter summarises the major findings of two studies regarding the use and impact of SEO and web analytics on news websites and journalism profession in Greece. Through examination of a sample of Greek journalists and several Greek news websites, it aims to provide new insights in the field of digital journalism.

The purpose of this paper is to provide an overview of NodeXL in the context of news diffusion. Journalists often include a social media dimension in their stories but lack the tools to get digital photos of the virtual crowds about which they write. NodeXL is an easy to use tool for collecting, analysing, visualising and reporting on the patterns found in collections of connections in streams of social media. With a network map patterns emerge that highlight key people, groups, divisions and bridges, themes and related resources.

This study conducts a literature review of previous empirical work which has utilised NodeXL and highlights the potential of NodeXL to provide network insights of virtual crowds during emerging news events. It then develops a number of guidelines which can be utilised by news media teams to measure and map information diffusion during emerging news events.

One emergent software application known as NodeXL has allowed journalists to take “group photos” of the connections among a group of users on social media. It was found that a diverse range of disciplines utilise NodeXL in academic research. Furthermore, based on the features of NodeXL, a number of guidelines were developed which provide insight into how to measure and map emerging news events on Twitter.

With a set of social media network images a journalist can cover a set of social media content streams and quickly grasp “situational awareness” of the shape of the crowd. Since social media popular support is often cited but not documented, NodeXL social media network maps can help journalists quickly document the social landscape utilising an innovative approach.

This is the first empirical study to review literature on NodeXL, and to provide insight into the value of network visualisations and analytics for the news media domain. Moreover, it is the first empirical study to develop guidelines that will act as a valuable resource for newsrooms looking to acquire insight into emerging news events from the stream of social media posts. In the era of fake news and automated accounts, i.e., bots the ability to highlight opinion leaders and ascertain their allegiances will be of importance in today’s news climate.

Human capital analytics (HCA) is integral to all other human capital processes. With a mature analytics practice, leaders can make better decisions more quickly and with greater confidences. This paper aims to describe results of research that shows how organizations in Middle East countries are investing in HCA. Specifically, it describes as follows: the extent to which they are investing; types of projects in which they are investing; and factors critical to making analytics work.

While research may include respondents from organizations in developing countries, only recently have efforts been made to monitor progress specifically in these countries. This paper attempts to describe the most recent findings of such research, paying specific attention to the use of HCA in the Middle East.

Organizations in the Middle East embrace HCA. While still in its infancy, analytics is poised to be a strategic driver that will lead to improved organizational performance.

Whether investing in leadership development, talent acquisition, employee engagement or talent development, analytics plays a central role in informing decisions about people investments. To make HCA work, Middle East organizations plan to continue building capability through training; embracing technology and striving to link data across programs and platforms; and integrating systems, processes and people with other functions, particularly finance. In the end, organizations will seamlessly integrate HCA into all processes to drive organization performance.

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

The study examines the relationship between news intensity, media sentiment and market microstructure invariance-implied measures of trading activity and liquidity of Chinese property developer stocks during the 2020–2022 Chinese property sector crisis.

The authors adopt the extension of the news article invariance hypothesis, which is a generalization of the market microstructure invariance conjecture, from January 2020 to January 2022 to test specific quantitative relationships between the arrival rate of public information, trading activity and a nonlinear function of a proxy for the probability of informed trading. Empirical tests are based on a dataset of 22,412 firm-day observations and two count-data models to correct for overdispersion and the excess number of zeros. Seventy-five stocks of Chinese companies from the property development industry (including the China Evergrande Group) were included in the sample.

The authors reject the news article invariance hypothesis but document a positive and significant relationship between the flow of public information and risk liquidity. Additionally, the authors find that the proxy for informed trading activity is positively related to the arrival rates of public information from October 2021 to January 2022.

The findings support the hypothesis that negative (positive) media sentiment induces significant deterioration (insignificant improvement) in stock liquidity. The authors find that an increase in the number of news articles about a company corresponds to a higher liquidity of Chinese property developers' stocks after controlling for media sentiment.

Clickbait has become a popular strategy for attracting online users by enticing them to follow the link to a particular website to read further. The purpose of this paper is to fill a gap in the literature by providing empirical evidence of how clickbait headlines affect online users’ emotional and behavioral responses, specifically emotional arousal and intention to read news. In addition, it is an early attempt to examine pupillary dilation response as an indicator of emotional arousal in the online news context.

An experiment was conducted primarily to examine the levels of emotional arousal evoked by two treatment groups of online news headlines, news and clickbait, compared to a neutral control group. Emotional arousal was assessed using two approaches – pupillary dilation response recorded by an eye-tracking device and the Self-Assessment Manikin (SAM) – and the results were compared. The influence of emotional arousal on intention to read news was hypothesized and tested.

The level of emotional arousal evoked by the headlines varies. In general, clickbait headlines generate a higher level of emotional arousal than do the neutral headlines but a lower level than the news headlines. The results also indicate that the level of emotional arousal measured by pupillary dilation response and by SAM are somewhat consistent. Emotional arousal appears to be a significant predictor of intention to read news.

This study is an initial attempt to investigate how clickbait headlines influence online users’ perceptions and responses, which will be of interest to researchers and news media publishers. The current study also provides evidence for adopting pupillary dilation response, an unobtrusive measure of emotional response, as an alternative methodology for future studies that investigate emotional arousal related to textual information in the online news context.