ABSTRACT
With the advent of COVID-19, the corporate world underwent a massive change. This stressed the need of effective employee engagement more than ever. Therefore, this study aims to understand the various dimensions of employee engagement and the use of Natural Language Processing to ensure an engaged workforce. To understand the problem, primary data collection was done with the help of interviews. The authors conducted interviews of 100 professionals from manufacturing, FMCG and IT companies in India to understand the problem from the root level. The paper then proposes a model, i.e., the PAUSE model which aims at categorizing the broad topic of employee engagement into five distinct categories. These categories tell us about the various areas in which employees can feel a sense of detachment or disengagement towards the organization. The model then suggests corrective actions that can be taken at the ground level to improve engagement in the targeted category. © 2022 IEEE.
ABSTRACT
The impact of COVID 19 on the economy in general is no doubt ravaging and its impact on agriculture is complex and varied across diverse segments that form the agricultural value chain. Cotton has a complex supply chain that stretch from input suppliers, farmers, traders, ginning factories, spinning mills, textile companies and oil processors. The study was designed to capture the panoramic view of world and national cotton economy during the pandemic period and its impact on cotton fanning in India. Cotton prices declined in the initial months for January to April, 2020 and later recouped once the lock down restrictions were phased out. As such from the study during the year 2020-2021, it was noticed in general, as per CAB estimates, cotton fanning in India was not Effected in its area and production excepting in north zone which was not due to lock down but for the pest attack and lack of irrigation facilities. Districtwise analysis confirmed that labour availability for loading and unloading and its transport was the major impediment especially in the southern zone while it was market uncertainty in the other zones. During the COVID 19 pandemic year, the cotton value chain, like others, had faced unprecedented disruptions. Cotton farmers and supply chain actors should work together to make sure that the farmers have secured acquaintance to sell their cotton. Farmers' protection should be considered a priority in getting the minimal requirements regarding the input supply, logistics and remuneration for their produce.
ABSTRACT
The spread of Fake News during this global pandemic COVID-19 has dangerous consequences on economy and health of public. From origin of virus, spread, self-medication to hoaxes on vaccination, it created more panic than the fatality of the virus. For better infodemic preparedness and control, it is necessary to mitigate fear among people, manage rumours, and dispel misinformation. A survey on Fake News during COVID-19 was made by Poynter Fact Check institute. It stated that major chunk of the fake news on COVID-19 originated majorly in Brazil, India, Spain, and the United States. Fake news menace is severe in countries where the trust on online media is high such as Brazil, Kenya and South Africa. Based on these observations, this study provides preliminary insight on the co-relation of the spatial and temporal meta-information of the news like the news source country, the name of the countries specified in the news, and date of publish of news to the credibility of news. The main contribution of this study is to analyse the impact of spatial and temporal information features for classification of fake news, which to the best of our knowledge has not been explored yet. Also, these features are directly not available in any news article available online. Hence, these features are handcrafted. Meta-data of the news article such as origin of news is considered. Additional spatial information is extracted from the news article using NER tagging. Temporal information such as date of origin of news is given as an input to the LSTM model. These features are given as an input to Long Short-Term Memory (LSTM) model along with GloVe vectors and word length vector. A comparative analysis for accuracy is tested of the models with and without spatial and temporal information. The model with spatial and temporal information has achieved noteworthy results in fake news detection. To ensure the quality of prediction, various model parameters have been tuned and recorded for the best results possible. In addition to accuracy, the spatial and temporal information for fake news detection offers several other important implications for government and policy makers that will be instrumental in simulating future research on this subject.
ABSTRACT
While the world is suffering from coronavirus pandemic (COVID-19), a parallel battle with Infodemic, the proliferation of fake news online is also taking place. The spread of fake news during this global pandemic COVID-19 has dangerous consequences. This is the driving force behind this study. Relying on incorrect information obtained from the internet or social media can be fatal. According to a World Health Organization survey, at least 800 people have lost their lives because of COVID-19 misinformation during this time, highlighting the accurate automated classification of fake news. However, the data at disposal for classification is imbalanced. The Internet has a vast repository of authentic healthcare news, whereas Fake News on COVID-19 healthcare is not abundant. This imbalance leads to incorrect classification. The paper studies alternative approaches to text sampling. In this paper, we propose a stance based sampling method for balancing news data. The disparity between the title and content of news items is utilized to sample data points selectively and rectify the imbalance. The key findings are that the proposed stance-based sampling strategies enhance categorisation task performance consistently for varying degrees of imbalance. The proposed techniques can better detect misleading news in the health care sector.
ABSTRACT
While the world is suffering from coronavirus pandemic (COVID-19), a parallel battle with Infodemic, the proliferation of fake news online is also taking place. The spread of fake news during this global pandemic COVID-19 has dangerous consequences. This is the driving force behind this study. Relying on incorrect information obtained from the internet or social media can be fatal. According to a World Health Organization survey, at least 800 people have lost their lives because of COVID-19 misinformation during this time, highlighting the accurate automated classification of fake news. However, the data at disposal for classification is imbalanced. The Internet has a vast repository of authentic healthcare news, whereas Fake News on COVID-19 healthcare is not abundant. This imbalance leads to incorrect classification. The paper studies alternative approaches to text sampling. In this paper, we propose a stance based sampling method for balancing news data. The disparity between the title and content of news items is utilized to sample data points selectively and rectify the imbalance. The key findings are that the proposed stance-based sampling strategies enhance categorisation task performance consistently for varying degrees of imbalance. The proposed techniques can better detect misleading news in the health care sector. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.