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 dataset includes information about different types of climate change adaptation interventions and their effects on different types of outcomes in the agricultural and coastal sectors in developing countries. METHODOLOGICAL INFORMATION Description of methods used for collection-generation of data: This systematic review selected studies that were already included in an evidence gap map (EGM) on adaptation, which is one of the most up-to-date and comprehensive databases on the effectiveness of adaptation interventions in low and middle-income countries (LMICs) (Doswald et al., 2019). The EGM followed the systematic map protocol, which followed guidelines set out by the Centre for Evidence-Based Conservation,(Pullin et al., 2018) and included quantitative or mixed-methods studies and systematic reviews in the analysis. The inclusion criteria for this meta-analysis were adapted from previous research(Doswald et al., 2019) following the PICOS standard (Eriksen & Frandsen, 2018) In a previous study (Doswald et al., 2019) we systematically searched databases of peer-reviewed literature (Web of Science, Scopus, 3ie database and CEE library) and grey literature from several organisational websites for studies on climate change adaptation in low- and middle-income countries (LMICs) as defined by the Organisation for Economic Co-operation and Development (OECD). All literature that had an English abstract and was written in English, Spanish, French or German was included. This yielded a sample of 13,121 studies. The sample was narrowed down by excluding books, book sections and conference proceedings and screening abstract and tiles following several exclusion criteria. Importantly for our purpose, all studies that did not report on the effectiveness of an adaptation intervention were excluded. This yielded a final set of 463 studies (Figure 4), which is published as an interactive EGM at the website of the International Initiative for Impact Evaluation (3ie) (Noltze et al., 2023). In the previous study (Doswald et al., 2019) we categorized studies into four sectors of i) Water, ii) Forestry, fishing and agriculture, iii) Land-use and built environment, and iv) Society, economy and health. Since we focused only on the coastal and agricultural sectors, we excluded 152 studies from their database that did not match these two sectors. We focused on the agricultural and coastal sectors for several reasons. The agricultural sector, along with the forest sector are most directly related with development in LMICs due to the importance of rural areas and the primary sector for those countries’ economies. The forestry sector is critical for climate change mitigation, but the impact of climate change on forest activities has been less documented than in the agricultural sector. The coastal sector has been pioneering in studies of climate change and additionally allowed us to capture intervention effects in urbanized areas. Also, interventions in the coastal sector have tended to target risk reduction outcomes, so by including those interventions we are able to widen the diversity of outcomes studied (the agricultural sector tends to include development-related outcomes). We also excluded primary or non-review studies on NbS or technological interventions in the agricultural sector, and studies which did not have sufficient data for coding. Primary studies on NbS or technological interventions in the agricultural sector were excluded due to the disproportionately large number of systematic reviews for these interventions, which we included in the review. This led to a final batch of 103 included studies, 19 and 84 of which belonged to the coastal and agricultural sectors, respectively. To code the data from the articles, we employed a rigorous qualitative consensus approach(Cascio et al., 2019) to ensure the reliability of our coding. This involved clear coding guidelines, regular communication among coders and iterative discussions to reach agreement. The coding included two stages. First, all three coders coded 6 studies collaboratively until agreement reached saturation; all coders coded the same study and discussed their codes, one study after the other, until coders reached a similar understanding of the variables (i.e., until coders had the same codes of the intervention type, outcome, and effects direction and size variables for two studies in a row). Then, the database of studies was split among the coders and each of them coded her/his batch independently. Questions at this stage were nevertheless solved collaboratively. This strategy enabled us to maintain a high level of coding consistency, enhancing the validity of our study's findings. The database had a hierarchical design: one study could include multiple observations, which were the combination of one intervention and one effect of this intervention in an outcome. Table 2 includes the final number of studies and observations per sector. To measure effects, we looked at the direction and size of effects (“Effects direction” and “Effect size” variables), and the statistical significance of the findings. Direction was coded as positive, neutral or negative. Neutral was coded when the effects were not significant, or the author explicitly mentioned that there were no effects. Effect size was coded via an ordinal scale (“small”, “medium”, “high”) whenever the effects direction was positive or negative (Creutzig et al., 2022) Coding effect sizes required translating the quantitative measures such as means, non-parametric tests, regression coefficients into our ordinal scale. Whenever author´s complemented quantitative metrics with qualitative comments about the size we used the latter. In the studies where authors did not qualify effects as being “small”, “medium” or “high” we assumed that the size of the effects was “medium". The only exception to this rule were observations where the metric value was very small (this was the case for <0.1 beta regression coefficients, mean differences, average treatment effects, and <5% percentage differences between the intervention and control groups). Out of the 103 studies of the database, 11 contained more than 5 observations per study (marked with an asterisk in Supplementary Table 1) and 9 of them contained more than 3 observations about one intervention and outcome type indicating the same effects direction (marked with two asterisks in Supplementary Table 1). Although many observations from single studies may be less generalizable, our unit of analysis was the case and not the study and we weighted all observations equally.

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Context

The Indian Premier League (IPL) is a professional Twenty20 cricket league in India usually contested between March and May of every year by eight teams representing eight different cities or states in India. The league was founded by the Board of Control for Cricket in India (BCCI) in 2007. The IPL has an exclusive window in ICC Future Tours Programme.

The IPL is the most-attended cricket league in the world and in 2014 was ranked sixth by average attendance among all sports leagues. In 2010, the IPL became the first sporting event in the world to be broadcast live on YouTube. The brand value of the IPL in 2019 was ₹475 billion (US$6.7 billion), according to Duff & Phelps. According to BCCI, the 2015 IPL season contributed ₹11.5 billion (US$160 million) to the GDP of the Indian economy.

Content

The dataset consist of data about IPL matches played from the year 2008 to 2019. IPL is a professional Twenty20 cricket league founded by the Board of Control for Cricket in India (BCCI) in 2008. The league has 8 teams representing 8 different Indian cities or states. It enjoys tremendous popularity and the brand value of the IPL in 2019 was estimated to be ₹475 billion (US$6.7 billion). So let’s analyze IPL through stats.

The dataset has 18 columns. Let’s get acquainted with the columns. - id: The IPL match id. - season: The IPL season - city: The city where the IPL match was held. - date: The date on which the match was held. - team1: One of the teams of the IPL match - team2: The other team of the IPL match - toss_winner: The team that won the toss - toss_decision: The decision taken by the team that won the toss to ‘bat’ or ‘field’ - result: The result(‘normal’, ‘tie’, ‘no result’) of the match. - dl_applied: (1 or 0)indicates whether the Duckworth-Lewis rule was applied or not. - winner: The winner of the match. - win_by_runs: Provides the runs by which the team batting first won - win_by_runs: Provides the number of wickets by which the team batting second won. - player_of_match: The outstanding player of the match. - venue: The venue where the match was hosted. - umpire1: One of the two on-field umpires who officiate the match. - umpire2: One of the two on-field umpires who officiate the match. - umpire3: The off-field umpire who officiates the match

Acknowledgements

• Data source from 2008-2017 - CricSheet.org and Manas - Kaggle
• Indian Premier League 2008-2019 Navaneesh Kumar - Kaggle
• Data source for 2018-2019 - IPL T20 - Official website

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              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.