The share of news consumers who actively try to avoid news was highest in Greece and Bulgaria as of early 2023, with 57 percent of respondents from each country saying they deliberately chose not to engage with news. Active news avoidance was also common in Argentina, Poland, and the United Kingdom, where the share of respondents saying they did so was over 40 percent.

A global study found that too much news about politics and COVID-19 ranked as the main reason for news avoidance, with 43 percent of respondents citing this as a factor leading them to often, sometimes, or occasionally refrain from keeping up to date with news. Meanwhile, 36 percent said they avoided news because of its negative affect on their mood, and others admitted they had news fatigue or found news to be untrustworthy or biased.

A global report held among industry leaders looking at trends in journalism and news revealed that 67 percent of respondents considered explanatory journalism to be very important in combating news avoidance and fatigue. Despite news avoidance often occurring due to audiences feeling overwhelmed or saddened by certain news topics, the idea of combating news fatigue with positive news stories was only considered to be an important initiative by 21 percent of publishers.

The data for this study were collected in november/december 2020 (wave 2 of longitudinal project; only wave 2 in this dataset) among children (8-13 years old) who filled out an online survey. The aim of our project was to unravel how mechanisms related to news consumption and news avoidance are related to one another. Therefore we measured children's news consumption, news avoidance, negative emotions, anxiety-related behaviors, parental mediation and reactive coping strategies. We used SEM in R to build the model and to test our hypotheses and RQ (see NoNewsTodayRScript for the script of our analyses). For more information about the method of this study, see NoNewsTodayMethods.

The variables were constructed by several items measured on likert-scales (see NoNewsTodayVariables for concise descriptions of these variables).

According to a 2023 survey, 75 percent of respondents from Finland who often avoided news were attempting to avoid news about the war in Ukraine. Audiences in neighboring Slovakia and Hungary were also trying to avoid news about the war, whereas U.S. consumers, the furthest away geographically, were the least likely to do so.

A global study held in early 2023 found that 53 percent of news avoiders did so by scrolling past news or changing channels when the news came on, making this the most common method of avoiding news. Others limited their news consumption to certain times of day or stopped notifications, whilst just over 30 percent avoiding specific topics that affected their mental wellbeing.

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

The data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards:

1. Immigrants, Refugees, Muslims, Hispanics, Venezuelans
2. News Media Consumption
3. Trust in News Media and Societal Institutions
4. Frequency and Valence of Intergroup Contact
5. Realistic and Symbolic Intergroup Threat
6. Right-wing Authoritarianism
7. Social Dominance Orientation
8. Political Efficacy
9. Personality Characteristics
10. Perceived COVID-threat, and
11. Socio-demographic Characteristics

1. Austria
2. Belgium
3. Germany
4. Hungary
5. Italy
6. Spain
7. Sweden

1. United States of America
2. Colombia

The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections.

A global report held among industry leaders exploring trends in journalism and news found that 72 percent of publishers were worried about the issue of news avoidance and fatigue among their audience due to certain topics making them feel overloaded or depressed. Only 13 percent expressed no concern about the matter.

A survey of adults in the United States conducted in early 2024 shows that more than 40 percent of respondents claim to avoid the news at least sometimes, if not often. The largest share of news avoiders fell among the 45-to-54 age group, however 18-to-34-year olds followed suit.

"The data in this spreadsheet were transcribed from the dataset referenced in Airbnb s Dec. 1, 2015, blog post. Because Airbnb did not allow the data to be downloaded, photographed, or copy-pasted, BuzzFeed News copied the data manually over a series of three visits with the company. Some of the worksheets have not been copied in full; ""[...]"" indicates that a particular column of data continues in the original, but were not transcribed. To the fullest extent possible, BuzzFeed News attempted to avoid transcription errors; some, however, may have snuck through."

In 2022, a global survey looking at four major markets found that news audiences in Brazil and the United States were the most likely to have seen criticism of the news from politicians in the last year. Close to 50 percent of respondents from each country said they had seen or heard politicians criticizing the news media. Meanwhile, a hefty share of respondents from both countries, as well as those in India and the United Kingdom, said that they heard ordinary people voicing negative opinions about journalist or news organizations – and this trend is likely to stick.

Consumers are distrustful of the news

From political bias to false information, to declining press freedom and artificial intelligence, there are a number of reasons why the public may be critical or suspicious of the news. Mis- and disinformation continue to pose problems to ordinary consumers seeking updates about key topics, and in the United States, the public generally feel that news organizations themselves are responsible for stopping the spread of false information. Should they be unable or unwilling to do this, trust in the news media will likely continue to fall.

Which steps can news publishers take?

In the face of growing news avoidance and fatigue, news publishers are deploying various strategies to combat it. Rather than attempt to reel audiences in with positive stories, the aim is to provide explanatory and solution-focused journalism to give consumers news they can use.

According to a global study on climate change news consumption in 2022, the respondents most likely to avoid climate change news were Indian adults aged 18 to 44 years old, with 43 percent saying they actively avoided news on the subject. Older Japanese consumers aged 45 years and above were the least likely to try to avoid climate news.

The world’s leading social media companies continue to grow at a remarkable rate – Facebook, for example, still boasts double digit growth in monthly active users, despite having passed the 2 billion mark some time ago – and expand into new lines of business, including Internet TV and online dating. However, they face a growing number of challenges, with regulatory bodies taking them to task on issues such as hate speech, fake news, tax avoidance, and data privacy.
The larger and more established firms in this sector should be strong enough, and diversified enough, to continue growing despite these obstacles, but they should not underestimate the threat that regulation poses, not least to their bottom line. Nor should they dismiss the very real concerns about social media’s ability to negatively impact society that are at the root of many of these regulatory initiatives. Read More

As of 2022, news avoidance was most common among Hungarians between the ages of 35 to 44 years old, measuring at 46 percent of the respondents. The lowest share of people sometimes or often avoiding news was recorded among Hungarians above the age of 54.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.

This special topic poll was conducted by ABC News and ESPN and sought respondents' views on Barry Bonds and the use of steroids in Major League Baseball. Respondents were asked to give their opinions on whether the use of steroids and performance-enhancing drugs was a problem in baseball. Respondents were further asked whether they thought Barry Bonds had utilized steroids or performance-enhancing drugs and whether that would have an impact on how they felt about him potentially breaking baseball's homerun record. Questions were also solicited regarding the possibility of Barry Bonds being elected to the Hall of Fame and whether the use of steroids or a conviction of tax evasion should prevent him from being selected. Demographic variables include race, gender, age, level of education, employment status, income, political party affiliation, political philosophy, and religious affiliation.

According to an October 2023 survey across all 27 European Union member states, respondents in Poland were the most engaged with national political news, with 70 percent of people aged 15 years and older saying they accessed news about national politics in the last week. A factor influencing this high level of engagement could arguably have been the Polish parliamentary election, which took place during the time period in which the survey was carried out. Meanwhile, only around a third of people in several countries were keeping up to date about national politics at this time, with Slovenia and Denmark ranking last in terms of engagement. 2024: the election year With elections due to take place in more than 60 countries across the globe in 2024, political news consumption will be in flux. Trust in the news media to accurately report election news is shaky among U.S. consumers, a sentiment likely to endure as left- and right-leaning publications vie for audience’s attention in the run-up to the 2024 presidential election. Confidence in world leaders is also prone to fluctuations and instability, and the many elections come at a time where humanitarian crises, international policy, and myriad more factors are already occupying the minds of potential voters. Political news engagement: fight or flight? One of the main reasons for news avoidance is politics. Audiences already feeling overwhelmed by information and fatigued by political stand-offs and debates may decide to switch off entirely as elections dominate the news. Alternatively, engagement with political news could see a spike as people seek to stay informed about major happenings in their own country and abroad. Another element which could affect political news access (and contribute not only to news fatigue but also to voting behavior) is misinformation. False political information remains an issue in countries around the world, and news publishers and consumers alike must therefore be vigilant.

U.S. companies circumvent Chinese tariffs by sourcing cobalt from Indonesia, with help from China's Lygend Resources, impacting the electric vehicle and defense sectors.

According to Cognitive Market Research, The Global Process Safety Services market size was USD 16.8 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 10.80% from 2023 to 2030. What are the Most Significant Opportunities and Factors Influencing the Process Safety Services Market?

Rising of Industrial Accidents Drives the Market Expansion

The increasing incidence of big and minor industrial accidents has heightened awareness about the necessity of process safety. Companies are investing in safety measures to avoid accidents, save downtime, and maintain a favorable public image because contemporary enterprises are more concerned with delivering maximum outcomes and manufacturing efficiency than with producing safely; the frequency of accidents and catastrophic incidents has grown over time. Several variables, each dependent on the context, commonly cause accidents.

For instance, according to data from BBC News, industrial accidents kill hundreds of individuals yearly and severely handicap thousands more. In 2021, a federal minister told parliament that over the previous five years, at least 6,500 employees had died while working in factories, ports, mines, and construction sites. Labor campaigners with years of experience in the sector told the BBC that the statistics may be higher because many events are not reported or documented.

(Source:www.bbc.com/news/world-asia-india-62631699)

Because Europe is a highly industrialized region that might be used as a case study to illustrate global industrialization, the statistics could imply a global trend in industrial accidents. Nonetheless, as workplace mishaps increase, the demand for a medium to reduce accidents grows more pressing. The respective factors will drive the process safety market.

Improving Factory Management and Product Efficiency is Becoming Increasingly Important

Process safety services help manage the integrity of hazardous material handling operational systems and processes. It can aid in detecting, comprehending, managing, and preventing process-related problems. If an event happens during the production process, it can have a negative impact on the manufacturing process and product efficiency. In an accident, the product may leak or be damaged. However, by implementing process safety solutions, product loss may be reduced, and industrial efficiency can be increased, leading to rapid growth in the process safety services market.

According to a Manufacturing Institute report, one of the Biggest Causes of supply chain disruptions is insufficient production planning, which leads to increased costs and delayed delivery. 74% of firms reported at least one supply chain interruption the previous year, with 39% directly attributing it to inadequate production planning.

(Source:www.deskera.com/blog/effective-production-planning-manufacturing/)

The Factors Are Limiting the Growth Of The Web Hosting Services Market

Budget allocations and a lack of competent labor Limit Market Growth

Among the key market restrictions are inadequate budget allocation mechanisms and a lack of competent labor. Labor skills are critical in guaranteeing the safety of any industrial process, regardless of its risks. As a result, a lack of trained labor may increase the number of accidents. Furthermore, small and medium-sized organizations' budgets for critical safety do not contain expenditures for precise and fast incident monitoring. As a result, the budget scope may not be able to handle information security, technology, and workplace health properly, resulting in competing goals and a lack of collaboration. As a result, there is a scarcity of experienced labor and a lack of budget allocation for safety process management in small to medium-sized businesses.

Impact of COVID-19 on the Process Safety Services Market

Most sectors throughout the world have been badly impacted in recent months. This is due to major interruptions in their separate manufacturing and supply-chain activities caused by different precautionary lockdowns and other limitations imposed by regulatory bodies throughout the world. Furthermore, consumer demand has reduced as individuals are now more focused on minimizing non-essential expenses from their separate budgets since the general economic state of most people has been negatively impacted by this pandemic.

The COVID-19 pandemic and global...