News Events Research Articles

Study on the Character Building Strategy of the Film Adapted from News Events

News events become one of the sources of inspiration for movies, which well interprets the view that “art comes from life”. In this paper, based on the analysis of numerous news events adapted films, combining with their own creative experience, we aims to explore the main character shaping strategy of news events adapted from films.

News and Trading After Hours

This paper explores after-hours trading (AHT) in U.S. equity markets. We collect a large set of news releases during AHT and document their effect on AHT activity and market quality. Three types of news events attract most AHT: earnings announcements, insider trades, and index reconstitutions. The majority of earnings announcements shift to AHT over time. Corporate insiders are more likely to delay their sales filings until markets close. Index reconstitutions during AHT lead to volume surge and contribute to the negative CAPM slope. During the Covid-19 pandemic, retail access (via Robinhood platform) leads to a sharp increase in after-hours trades.

Temporal Selective Exposure: How Partisans Choose When to Follow Politics

It is widely theorized that echo chambers contribute to polarization, yet behavioral evidence of partisan selective exposure in the real world is surprisingly tenuous. Why do partisans have polarized perceptions even though they have relatively balanced media diets? We argue that partisans vary in terms of when they pay attention to the news, not just in terms of the ideological media sources they follow. By leveraging national election surveys across seven decades, as well as the sudden change in the economic news environment that was induced by the collapse of Lehman Brothers, we show that partisans vary their political attentiveness and media consumption in response to whether news events are congenial to their party. These findings suggest that partisans can subject themselves to biased information flows even if their media diets are balanced. The temporal dynamics of selective exposure carry important implications for mass polarization.

Story character, news source or vox pop? Representations and roles of citizen perspectives in crime news narratives

This study analyzes citizen representations in a corpus of 300 Dutch newspaper narratives published between 1860 and 2009. Results show that citizen perspectives are more frequently represented than authority perspectives, although the perspectives of authorities have become somewhat more frequent over time. In-depth analyses of the citizen perspectives show that citizens may fulfil multiple roles in the crime narratives, leading up to a functional typology of citizens as (1) story characters experiencing the news events, (2) news sources providing inside information about the events, and (3) vox pops expressing opinions and evaluations of the events. The variety of citizen perspectives included in crime news narratives and the multitude of roles they fulfill may help audience members to become informed as well as engaged and to explore their personal emotions, which may ultimately reinforce moral, cultural and societal values.

Thanks, neighbor! Stock market experience around significant European tax cuts

PurposeThis paper analyzes the effect of corporate tax cuts on the competitiveness of the tax-cutting countries and neighbor countries.Design/methodology/approachThis study utilizes four significant corporate tax reforms among the OECD countries in Europe that offer a one-time tax cut of 6% or more. The short-term event study approach examines the stock index reactions for both the tax-cutting countries and the other countries. Multivariate fixed-effect regressions are employed to study the cross-sectional variations in the non-tax-cut countries.FindingsThis paper finds positive excess returns for Slovakia and Germany around the tax-cut passage. Multivariate analysis of stock market reactions of the non-tax-cutting countries reveals some evidence supporting both the positive spillover effect and the negative competitive loss effect. More advanced countries are more likely to experience higher abnormal returns, while higher tax countries are more likely to suffer lower abnormal returns. Other factors identified that might have influenced the effect of a foreign tax cut include the existing trade flows with the tax-cutting countries, whether the country has a common currency and the export orientation of the economy.Research limitations/implicationsThe findings are subject to sample-size issues. The lack of results for the other two countries is due to complicating events, as suggested by the further investigation of concurrent news events around the event days.Practical implicationsThe simultaneous analysis of the reform countries and the other countries in the region suggests that policymakers need to consider the relative positioning of their country vs the other countries in terms of economic development and current tax burdens when determining the optimal policy for their country or to respond to the tax policy changes in the other countries.Originality/valueThis study offers empirical evidence regarding the effect of corporate tax changes on competitiveness through the lens of stock markets' reactions, which depend on the net results of the spillover gain vs the competitive loss.

Stochastic Diffusion Model for Analysis of Dynamics and Forecasting Events in News Feeds

One of the problems of forecasting events in news feeds, is the development of models which allow for work with semi structured information space of text documents. This article describes a model for forecasting events in news feeds, which is based on the use of stochastic dynamics of changes in the structure of non-stationary time series in news clusters (states of the information space) on the basis of use of diffusion approximation. Forecasting events in a news feed is based on their text description, vectorization, and finding the cosine value of the angle between the given vector and the centroids of various information space semantic clusters. Changes over time in the cosine value of such angles between the above vector and centroids can be represented as a point wandering on the [0, 1] segment. This segment contains a trap at the event occurrence threshold point, which the wandering point may eventually fall into. When creating the model, we have considered probability patterns of transitions between states in the information space. On the basis of this approach, we have derived a nonlinear second-order differential equation; formulated and solved the boundary value problem of forecasting news events, which allowed obtaining theoretical time dependence on the probability density function of the parameter distribution of non-stationary time series, which describe the information space evolution. The results of simulating the events instance probability dependence on time (with sets of parameter values of the developed model, which have been experimentally determined for already occurred events) show that the model is consistent and adequate (all the news events which have been used for the model verification occur with high values of probability (within the order of 80%), or if these are fictitious events, they can only occur over the course of inadmissible long time).

Renewal model for anomalous traffic in Internet2 links

We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical properties of the times between the arrivals of the anomalies, their duration and stochastic structure. Anomalous, or active, time periods are modelled as periods containing clusters or 1s, where 1 indicates a presence of an anomaly. The inactive periods consisting entirely of 0s dominate the 0–1 time series in every link. Since the active periods contain 0s, a separation parameter is introduced and estimated jointly with all other parameters of the model. Our statistical analysis shows that the integer-valued separation parameter and five other non-negative, scalar parameters satisfactorily describe all statistical properties of the observed 0–1 series.

The role of media in mergers and acquisitions

Using a novel dataset of news events for 170,000 entities across over 100 countries, we examine how media sentiment could affect firm acquisition decisions. Studying 77,552 completed merger and acquisition (M&A) deals around the world from 2000 to 2015, we find that firms with high media sentiment are more likely to become an acquirer and to pay a higher deal premium. The effect of media on the likelihood of an acquisition is stronger for cross-border deals, in countries with higher governance standards, larger firms with fewer financial constraints, and firms with higher market-to-book ratio and more media coverage. Target firms typically have low media sentiment. Acquirers with high media sentiment experience significantly positive returns prior to acquisitions and negative returns post-acquisition, inconsistent with theories of media content as a proxy for new information about fundamental asset values. Overall, we show that the media play an important role in the market for M&As.

Decision-making and computational linguistic tools application for overall estimation of the level of social tension

The study of social tension, its causes and laws of variation, as well as establishing its level is crucial during crises, epidemics and wars. The purpose of this research is to develop the system for determining the level of tension in society on the basis of data from social networks using decision-making and computational linguistic tools. The impact of various factors on the growth of social tensions is determined with the help of knowledge bases built by means of decision support systems. To analyse the emotionality of comment on news publications the TF-IDF and Word2vec methods are used. Within the framework of the method of goal dynamic estimating of alternatives, the level of social tension is determined as the degree of achievement of the main goal, the ratings of news publications and the ratings of news events that contributed to the increase in social tension are calculated. The study presents the efficient method of determining the level of social tension and defining the main factors that have the greatest impact on the growth of social tension. Further studies are needed to improve the estimates of factors and the overall estimate of social tension.

Network centrality, connections, and social capital: Evidence from CEO insider trading gains

AbstractChief executive officer's (CEO's) insider trading gains are affected by the position of the CEO within the hierarchy of all executives, as assessed by network centrality. CEOs with high centrality earn superior abnormal returns following their company's stock purchases, consistent with social capital advantage. Social capital and trading gains are positively associated primarily in firms that are riskier, have weak governance, or are managed by CEOs with no background in finance. High‐centrality CEOs also gain by selling their shares prior to a bad news event experienced by their firm. Finally, trading gains are positively affected by CEOs having past connections to the chief financial officers.

Ensemble deep learning framework for stock market data prediction (EDLF-DP)

Stock market variation data is collected in one form of breaking news from various finance web sites. The free financial data about companies is available on Internet Portals .The stock market sentiment changes with at a fraction due to major financial reforms, weather, natural disasters, and news events. Online finance news generates large amount of data. The market reforms are predicted with various machine learning algorithms. The term frequency-inverse document frequency (TF-IDF) features extracted from online news data for various companies of Bombay Stock Exchange are used along with other stock market features for prediction. The next day's stock price is predicted using ensemble deep learning framework. The data set is optimized by various deep learning techniques to get more accurate results. The proposed model produces approximately 85% of accurate prediction with deep learning framework. The market trends in terms of high and low stock values are matching exactly. The results can be further improved with use of high frequency trading algorithms.

Renewal model for anomalous traffic in Internet2 links

We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical properties of the times between the arrivals of the anomalies, their duration and stochastic structure. Anomalous, or active, time periods are modelled as periods containing clusters or 1s, where 1 indicates a presence of an anomaly. The inactive periods consisting entirely of 0s dominate the 0–1 time series in every link. Since the active periods contain 0s, a separation parameter is introduced and estimated jointly with all other parameters of the model. Our statistical analysis shows that the integer-valued separation parameter and five other non-negative, scalar parameters satisfactorily describe all statistical properties of the observed 0–1 series.

Disinformation creep: ADOS and the strategic weapon-ization of breaking news

In this essay, we conduct a descriptive content analysis from a sample of a dataset made up of 534 thousand scraped tweets, supplemented with access to 1.36 million tweets from the Twitter fire-hose, from accounts that used the #ADOS hashtag between November 2019 and September 2020. ADOS is an acronym for American Descendants of Slavery, a largely online group that operates within Black online communities. We find that the ADOS network strategically uses breaking news events to encourage discourage Black voters from voting for the Democratic party, a phenomenon we call disinformation creep. Conversely, the ADOS network has remained largely silent about the impact of the novel coronavirus on Black communities, undermining its claims that it works in the interests of Black Americans.

An Information-Event Perspective to CEO Performance Evaluation

It has been well-established that both stock prices and accounting earnings are used to evaluate and compensate CEOs. Prior studies often interpret the higher sensitivity of compensation revisions to stock prices (relative to accounting earnings) as the superior role of price formation versus financial reporting in conveying information about CEO effort. Using an information perspective that parses out firm-initiated news events during the year when stock prices impound the future implications of the information released in these events, we show that a significant part of the explanatory power of stock returns for CEO compensation occurs during trading days when financial accounting information is publicly released to the market. Further, the greater pay-for-performance sensitivity of information-event returns for CEO compensation is pronounced for events that are informative about CEO effort and especially those that are associated with accounting disclosures. Additional cross-sectional tests based on shocks to auditor reputation, internal control weaknesses, and large shareholder attention demonstrate the role of financial reporting credibility and corporate governance in our inferences. Overall, our study illustrates the co-dependence between price-based and accounting-based measures for CEO performance evaluation, and presents a framework that exploits this complementarity.

Verificación de hechos: Falibilismo de Popper y la razonabilidad de las masas

La verificación de información es parte del ejercicio periodístico, esta puede pasar por un proceso de falibilismo, sin embargo, muchos factores restan credibilidad a la profesión. El objetivo del artículo es identificar cómo la ciudadanía, en especial los futuros comunicadores, asumen la labor del ejercicio periodístico, con respecto a sus rutinas productivas, si este le brinda tratamiento al hecho noticioso, y de qué manera las personas procesan la información. Es importante destacar cómo los sujetos disciernen los contenidos noticiosos. El trabajo presenta un enfoque mixto de investigación; cuali-cuantitativo, la población objeto de estudio está conformada por estudiantes pertenecientes a dos universidades del Ecuador. Se aplicó el tipo de muestreo no probabilístico y entre las conclusiones se pueden desatacar que la credibilidad de la actividad, desde sus preceptos éticos y de responsabilidad social, está en tela de juicio. Además, el entorno digital actual está distorsionando las funciones sustantivas que conlleva la profesión, mientras tanto, la ciudadanía está optando por consumir contenidos sin profundizar en un pensamiento crítico coherente. Existe un incremento exponencial de noticias falsas y poca contrastación de información y, finalmente, la inmediatez dentro de los medios de comunicación está superando a la confirmación de los hechos que se publican. Los resultados denotan un futuro poco alentador en el entorno de esta labor, pero que con nuevos replanteamientos podrían cambiar.

Exploring the Impact of Delivery Mistakes, Gender, and Empathic Concern on Source and Message Credibility

ABSTRACT This study explores the impact of television news anchor delivery mistakes on message and source credibility evaluations and whether gender, news context, and empathic concern moderate this effect. The results of an experiment showed that viewers have the highest appraisal of anchors with perfect delivery, and evaluation suffers precipitously with increased mistakes. When evaluating short news clips, viewers were more tolerant of the female news anchor making three delivery mistakes than the male, but both genders suffered equally when the number of mistakes increased to five. The impact of delivery mistakes on credibility evaluations was consistent in both morning and evening news. Finally, viewers with a low level of empathetic concern reported a harsher view of anchors making mistakes than viewers with a high level of empathetic concern. However, experiences of parasocial interactions evaluation suffered the most in the three delivery mistakes condition and saw an increase in the five mistakes condition.

Tweets and transitions: Exploring Twitter-based political discourse regarding energy and electricity in Ontario, Canada

The article explores how Twitter data can inform the study of the socio-political dimensions of sustainability transitions. Twitter is a widely used microblogging platform that allows users to share short comments, media, and links, and that offers researchers significant data collection opportunities. Twitter-based research has been growing in application in many disciplines but has not been prominently used in relation to sustainability transitions or sustainable energy research. This study aims to characterize the Twitter-based conversations regarding energy issues and politics in Ontario, Canada. The analysis in this article is based on 6946 tweets, from 2841 unique users, which were collected between September 2, 2017 and January 12, 2018. The Twitter-based discourse regarding energy issues in Ontario is described by a minority of very engaged users contributing disproportionately to the conversation, the most engaged users contributing different types of tweets to the conversation, and overall engagement that varies based on news events. Coding based on manual interpretation of the tweets by the most engaged users and those tweets that were highly retweeted identified a discourse that was highly partisan and often highlighted economic issues associated with electricity costs. Topics commonly associated with sustainable energy transitions were not prominent in the Twitter discourse. Additionally, the analysis suggests that users lacking traditional political empowerment can influence the political discourse on Twitter through high levels of retweets; however, savvy and strategic use of Twitter communication, rather than simply engagement with an issue, is important in generating consistent amplification from other users.

Church communication highlights 2020

The year 2020 will go down in history as the year of COVID-19, even in the realm of the Catholic Church. Among other things, the pandemic led the Pope to forgo all pastoral trips outside of Italy. During the twelve months of that “fateful” year, however, other events that were relevant to the life of the Church did take place. In the following pages, we shall list some of the most important news stories covered by the media. Since there is no other way, the selection is based on this author’s criteria, though it does rely on some objective grounds: the “rivers of ink”, as they used to say, that each of these events unleashed. In other words, we have selected those news events that sparked the most conversation and discussion.

Diffusion Model for Forecasting Events in News Feeds

On the basis of the diffusion theory, we suggested a model for forecasting event in news feeds, which is based on the use of stochastic dynamics of changes in the structure of non-stationary time series in news text clusters (states of the information space). Forecasting events in a news feed is based on their text description, vectorization, and finding the cosine value of the angle between the given vector and the centroids of various information space semantic clusters. Changes over time in the cosine value of such angle between the above vector and centroids can be represented as a point wandering on [0,1] segment. This segment contains a trap at the event occurrence threshold point. The wandering point can fall into this trap over time. We have considered probability patterns of transitions between states in the information space. We have derived a nonlinear second-order differential equation; formulated and solved the boundary value problem of forecasting news events. We have obtained theoretical time dependence for the probability density function of the parameter distribution of non-stationary time series that describe the information space evolution. The results of simulating the time dependence of the event probability (with sets of parameter values of the developed model, which have been experimentally determined for already occurred events) show that the model is consistent and adequate. Experimental verification of the proposed model was carried out using a corpus of texts written in Russian.

Forecasting Crude Oil Price Using Event Extraction

Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.