This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

The DatasetMH.xlsx excel corresponds to a corpus of mental health in social networks labelled with polarity and stigma. In particular, the corpus consists of 2,287 comments labelled with polarity (positive, negative, neutral) and stigma from comments on Instagram posts about celebrity mental health disclosures:

1. Polarity: It consists of giving a positive, negative or neutral/undefined value to the comments in response to the disclosure or description of the symptomatology in the post. Positive polarity reflects understanding, encouragement or even admiration of the publication. E.g., “Cheer up, we love you".”. Negative polarity is assigned when the person expresses negative opinions, usually questioning the post with ironic, sarcastic or even mocking and disparaging comments. E.g., “how you show that you don't know what depression or anxiety is, shame on you!”. Neutral or undefined polarity is assigned in cases where no clear opinion is detected or can be interpreted in both directions. E.g., “take medication, it will help you" "and your partner?”
2. Stigma: stigmatising responses to comments are behaviours in which negative beliefs and emotions towards MH problems are expressed. Stigma manifests in a variety of forms including rejection and anger against the person, which may extend to contempt or mockery, belittling their problem. E.g.,"What a desire to draw attention to yourself"; "what you have is a story"; "you're so inconsistent and seeking the limelight". Because socially we know that "stigma is wrong" many rejection comments are made in an ironic or sarcastic way. E.g., and how do you write on insta?"; "better information from someone who doesn't have a current account". Additionally, anger is shown by arguing that such posts "trivialise or commercialise" MH. E.g.,"don't come and tell me your false stories of overcoming, without even knowing what it is to work...". Other times the stigma manifests itself as pity or sorrow for the person. E.g.,“It breaks my heart”; “poor thing”.

The file DatasetMH_Emotions.xlsx corresponds to a corpus of mental health in social networks labelled with emotions. In particular, the corpus consists of 2,287 comments labelled with five emotions plus a neutral class from comments on Instagram posts about celebrity mental health disclosures. These emotions are:

• Love/admiration: This emotion involves messages where admiration, approval and love are closely related.
• Gratitude: the messages imply a sincere appreciation for sharing mental health-related content on social networks.
• Comprehension/empathy/identification: The messages involve interest in and understanding of the message, including self-identification with the situation or context.
• Sadness: This primary emotion is produced by events that are not pleasant and that denote heaviness. It includes many manifestations of pity for the person.
• Anger/contempt/mockery: This emotion involves responses of irritation and attacks on the person as ridiculous and superficial.
• Neutral: This category corresponds to messages without emotions.

The labelling process of both datasets was divided into two phases: an initial phase with a pilot corpus (N = 787 comments) and a second phase focused on the development of the corpus with all the comments of the selected posts (N = 21151). The same methodology was followed in both phases: once the comments were collected, the corpus was cleaned, and then two independent experts were responsible for labelling each category. A third expert then reviewed the comments to resolve discrepancies. In the third and final phase, a final corpus for application to the machine learning algorithms is built from the large corpus (N = 2287).

Classification models are a set of machine learning algorithms developed to assess emotional response, i.e. polarity, stigma and emotions in social networks, based on previously developed datasets (DatasetMH_Emotions.xlsx, DatasetMH.xlsx).

Instagram’s most popular post

              As of April 2024, the most popular post on Instagram was Lionel Messi and his teammates after winning the 2022 FIFA World Cup with Argentina, posted by the account @leomessi. Messi's post, which racked up over 61 million likes within a day, knocked off the reigning post, which was 'Photo of an Egg'. Originally posted in January 2021, 'Photo of an Egg' surpassed the world’s most popular Instagram post at that time, which was a photo by Kylie Jenner’s daughter totaling 18 million likes.
              After several cryptic posts published by the account, World Record Egg revealed itself to be a part of a mental health campaign aimed at the pressures of social media use.

              Instagram’s most popular accounts

              As of April 2024, the official Instagram account @instagram had the most followers of any account on the platform, with 672 million followers. Portuguese footballer Cristiano Ronaldo (@cristiano) was the most followed individual with 628 million followers, while Selena Gomez (@selenagomez) was the most followed woman on the platform with 429 million. Additionally, Inter Miami CF striker Lionel Messi (@leomessi) had a total of 502 million. Celebrities such as The Rock, Kylie Jenner, and Ariana Grande all had over 380 million followers each.

              Instagram influencers

              In the United States, the leading content category of Instagram influencers was lifestyle, with 15.25 percent of influencers creating lifestyle content in 2021. Music ranked in second place with 10.96 percent, followed by family with 8.24 percent. Having a large audience can be very lucrative: Instagram influencers in the United States, Canada and the United Kingdom with over 90,000 followers made around 1,221 US dollars per post.

              Instagram around the globe

              Instagram’s worldwide popularity continues to grow, and India is the leading country in terms of number of users, with over 362.9 million users as of January 2024. The United States had 169.65 million Instagram users and Brazil had 134.6 million users. The social media platform was also very popular in Indonesia and Turkey, with 100.9 and 57.1, respectively. As of January 2024, Instagram was the fourth most popular social network in the world, behind Facebook, YouTube and WhatsApp.

Description of the Dataset 1. Dataset Overview

Name: Wellness Technology Market Analysis Dataset Purpose: This dataset is designed to analyze various factors influencing the success of wellness technology companies. It aims to identify strategic opportunities and challenges in the wellness tech industry by evaluating market trends, customer behavior, and competitive dynamics. 2. Key Attributes

Company ID: A unique identifier for each wellness technology company. Company Name: The name of the company. Product Categories: Types of wellness products offered (e.g., wearables, fitness apps, mental health platforms). Market Share: Percentage of market share held by the company in different regions. Revenue: Annual revenue generated by the company (numerical, in USD). Customer Satisfaction Score: Average customer satisfaction ratings (numerical, e.g., 1 to 10 scale). Investment Amount: Total investment received by the company (numerical, in USD). Product Features: Key features of each product (categorical, e.g., heart rate monitoring, sleep tracking). Competitive Position: Assessment of the company’s position relative to competitors (categorical, e.g., leader, challenger, niche). Innovation Index: An index score representing the level of innovation in the company’s product offerings (numerical). Marketing Spend: Annual expenditure on marketing and promotional activities (numerical, in USD). User Demographics: Age, gender, and location of the users (categorical and numerical). 3. Data Collection Method

Sources: The data was collected from a combination of primary and secondary sources:

Industry Reports: Data was sourced from market research reports and industry analysis published by organizations like Gartner, IDC, and Statista.

Company Financial Statements: Financial information and market share data were obtained from public financial reports and investor relations sections of company websites.

Customer Reviews and Ratings: Customer satisfaction scores and feedback were collected from review platforms such as Trustpilot, Google Reviews, and app store ratings.

Surveys and Interviews: Direct surveys and interviews with industry experts, company executives, and customers were conducted to gather qualitative insights into product features and competitive positioning.

Market Analysis Tools: Tools like Google Trends and social media analytics were used to assess market trends and consumer sentiment.

Collection Tools and Techniques:

Web Scraping: Automated scripts were used to extract data from online reviews and financial websites. APIs: Data was pulled from APIs provided by financial databases and market analysis tools. Surveys: Surveys were administered using platforms like SurveyMonkey to gather direct feedback from stakeholders. Data Quality Assurance:

Data Cleaning: Involves handling missing values, correcting data inconsistencies, and ensuring accurate data entry. Validation: Data was cross-verified with multiple sources to ensure reliability and accuracy. 4. Dataset Size and Format

Size: The dataset comprises data from [number of companies, e.g., 50] wellness technology companies and covers [number of records, e.g., 500] individual data points. Format: The data is stored in [format, e.g., Excel spreadsheets, SQL database] for ease of analysis and integration with analytical tools. 5. Privacy and Compliance

Data Privacy: All data collected is anonymized to ensure the privacy of individuals and companies. Compliance: The data collection process adheres to relevant data protection regulations such as GDPR and CCPA, ensuring proper consent and secure handling of data.