Timeliness Of News Research Articles

Unbiased news recommendation model combining time and content

The influence of popularity bias on news recommendation systems is substantial, as it increased the exposure of popular news and their being pushes to irrelevant users frequently, leading to less-than-ideal performance of the system. Existing popularity debiasing methods aim to optimize the fairness of recommendations so that unpopular news have more chances to be recommended. However, they ignore the timeliness that characterizes news, and some outdated news are recommended to users due to their low popularity. Unfortunately, users are not interested in outdated news. In addition, existing debiasing methods use content-independent popularity computation, which cannot accurately capture the dynamic changes in news popularity, thus reducing the effectiveness of debiasing. In this research, we introduce an innovative method named Time and Content-aware Causal Model, referred to as TCCM, which mitigates the popularity bias using current causal inference techniques that work well. The difference is that, first, we design a novel causal graph to analyze user interaction considering that news is timeliness. Based on this causal graph, TCCM models the influences of three factors on user interaction, namely, the timeliness of news, the popularity of news, and the matching between the content of news and the interest of the user, in order to solve the problem of recommending outdated news to users after de-biasing. Second, we consider the influences of news content on popularity and propose a new popularity estimation method. In TCCM, we improve the debiasing effect by combining news content (entity and word popularity) to obtain more accurate news popularity. Extensive experiments conducted on widely recognized benchmark datasets show that TCCM surpasses several cutting-edge approaches.

Analysis of the Influence of Information and Communication Technologies on News Processing, Reporting and Dissemination on Private Radio Stations in Nigeria: Sun Rays 103.1 FM, Korinya City in Focus.

Information and Communication technologies is considered to be a driving force to be reckoned with in the 21st century because it has caused and continues to cause major changes in the way we live. In the electronic media, ICTs has ignited and provoked radical and drastic changes that has affected and revolutionized the broadcast industry, most especially in immediacy and timeliness of news. With Information and Communication Technologies, information spread, infinitely becomes faster and cheaper and readily available. This study attempts to analyze the influence of information and communication technologies in news processing and reporting on private broadcast stations in Nigeria with emphasis on Sun Rays FM, Korinya City. The Research Design employed is the descriptive survey design. Three research questions were formulated for the study. The population for the study consisted of 380 respondents. Questionnaire was used as the instrument for gathering data for the study. Data collected were analyzed using frequency tables and simple percentage. This study uncovered some of the numerous benefits and challenges associated with the use of ICTs in news processing in broadcast industry. This study concludes that ICTs have created opportunities for widespread electronic and timely delivery of news. Furthermore, not only are there technology barriers that news organizations need to overcome to deliver news electronically, but there are also problems of employee attitudes toward new technologies and resistance to change. The implication of the use ICTs to broadcast stations might lead to an overall shrink in the need for human correspondents.

DNCP: An attention-based deep learning approach enhanced with attractiveness and timeliness of News for online news click prediction

Predicting news clicks or popularity is of great importance to news providers and recommender systems. Attractiveness and timeliness of news are two prominent drivers of news clicks. Attractiveness represents the appealingness or interestingness of news to an individual, while timeliness indicates the recency of news. Existing research on news click prediction models has ignored those two important variables. There is a lack of exploration and understanding of how to represent them in a predictive model and how effective and valuable they are to the prediction performance of a model. To fill these gaps, in this research, we propose a deep news click prediction (DNCP) model that integrates attractiveness and timeliness of news in an attention-based deep neural network for predicting news clicks. We also propose new measures for those two variables. Empirical evaluation using two real-world datasets shows that the DNCP model outperforms a variety of baseline models. The findings of this research provide several novel research contributions and practical implications for improving news click prediction.

Edge Computing and Blockchain for Quick Fake News Detection in IoV.

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.

Do analysts affect bad news timeliness?

I investigate the effect of analysts on the speed with which bad news is reflected in earnings. Intuitively, the more analysts that cover a firm, the more costly it will be for the firm to keep bad news suppressed. Thus, analyst coverage should positively affect bad news timeliness (BNT) (but not necessarily the differential timeliness of bad news over good news, or conditional conservatism). Using brokerage house mergers as a natural experiment with a difference-in-differences design, I find that an exogenous decrease in analyst coverage decreases BNT; that is, analysts positively affect BNT. The decrease in BNT is robust to controlling for unobserved firm heterogeneity, using a propensity score matched sample, persists for up to three years after the brokerage house merger, and is stronger for firms with relatively low analyst coverage before the merger. The result improves our understanding of how analysts affect a firm's information environment.

Study of User Interest Model Based on News Recommendation

In order to improve the accuracy of news recommendation, this paper established a comprehensive user interest model. First,this paper established a stable user interest model based on user browsing habits .Then,the paper also advanced freshness-based tentative recommendations on the basis of news timeliness and mainstream to get the user's temporary interest model. Finally,the paper combined these two models to establish a comprehensive user interest model. Experimental results proved that the proposed method can recommend specific news articles that best meet the user's reading preferences from a large number of the latest published news.