The ongoing COVID-19 pandemic has posed a severe threat to public health worldwide. In this study, we aimed to evaluate several digital data streams as early warning signals of COVID-19 outbreaks in Canada, the US and their provinces and states. Two types of terms including symptoms and preventive measures were used to filter Twitter and Google Trends data. We visualized and correlated the trends for each source of data against confirmed cases for all provinces and states. Subsequently, we attempted to find anomalies in indicator time-series to understand the lag between the warning signals and real-word outbreak waves. For Canada, we were able to detect a maximum of 83% of initial waves 1 week earlier using Google searches on symptoms. We divided states in the US into two categories: category I if they experienced an initial wave and category II if the states have not experienced the initial wave of the outbreak. For the first category, we found that tweets related to symptoms showed the best prediction performance by predicting 100% of first waves about 2–6 days earlier than other data streams. We were able to only detect up to 6% of second waves in category I. On the other hand, 78% of second waves in states of category II were predictable 1–2 weeks in advance. In addition, we discovered that the most important symptoms in providing early warnings are fever and cough in the US. As the COVID-19 pandemic continues to spread around the world, the work presented here is an initial effort for future COVID-19 outbreaks.

As per the results of a survey on the impact of the coronavirus (COVID-19) pandemic on media usage across India, there was a spike in usage of social networking applications in the first phase of the nation-wide lockdown. This stabilized in the following weeks with individual users reporting an average * hours and ** minutes on social media in the last week of June that year.

This dataset focuses on Twitter engagement metrics related to the Coronavirus disease (COVID-19), an infectious disease caused by the SARS-CoV-2 virus [1]. It provides a detailed collection of tweets, including their text content, the accounts that posted them, any hashtags used, and the geographical locations associated with the accounts [1]. The dataset is valuable for understanding public discourse, information dissemination, and engagement patterns on Twitter concerning COVID-19, particularly for analysing how people experience mild to moderate symptoms and recover, or require medical attention [1].

Columns

• Datetime: Represents the exact date and time a tweet was posted [2].
• Tweet Id: A unique identifier assigned to each tweet [2].
• Text: The actual content of the tweet [2].
• Username: The display name of the tweet author [2].
• Permalink: The direct link to the tweet on Twitter [2].
• User: A link to the author's Twitter account [2].
• Outlinks: Any external links included within the tweet [2].
• CountLinks: The number of links present in the tweet [2].
• ReplyCount: The total number of replies to that specific tweet [2].
• RetweetCount: The total number of retweets of that specific tweet [2].
• DateTime Count: A daily count of tweets, aggregated by date ranges [2].
• Label Count: A count associated with specific ranges of tweet IDs or other engagement metrics, indicating the distribution of tweets within those ranges [3-5].

Distribution

The dataset is structured with daily tweet counts and covers a period from 10 January 2020 to 28 February 2020 [2, 6, 7]. It includes approximately 179,040 daily tweet entries during this timeframe, derived from the sum of daily counts and tweet ID counts [2, 3, 6-11]. Tweet activity shows distinct peaks, with notable increases in late January (e.g., 6,091 tweets between 23-24 January 2020) [2] and a significant surge in late February, reaching 47,643 tweets between 26-27 February 2020, followed by 42,289 and 44,824 in subsequent days [7, 10, 11]. The distribution of certain tweet engagement metrics, such as replies or retweets, indicates that a substantial majority of tweets (over 152,500 records) fall within lower engagement ranges (e.g., 0-43 or 0-1628.96), with fewer tweets showing very high engagement (e.g., only 1 record between 79819.04-81448.00) [4, 5]. The data file would typically be in CSV format [12].

Usage

This dataset is ideal for: * Data Science and Analytics projects focused on social media [1]. * Visualization of tweet trends and engagement over time. * Exploratory data analysis to uncover patterns in COVID-19 related discussions [1]. * Natural Language Processing (NLP) tasks, such as sentiment analysis or topic modelling on tweet content [1]. * Data cleaning and preparation exercises for social media data [1].

Coverage

The dataset has a global geographic scope [13]. It covers tweet data from 10 January 2020 to 28 February 2020 [2, 6, 7]. The content is specific to the Coronavirus disease (COVID-19) [1].

License

CC0

Who Can Use It

This dataset is particularly useful for: * Data scientists and analysts interested in social media trends and public health discourse [1]. * Researchers studying information spread and public sentiment during health crises. * Developers building AI and LLM data solutions [13]. * Individuals interested in exploratory analysis and data visualization of real-world social media data [1].

Dataset Name Suggestions

• COVID-19 Twitter Engagement Data
• SARS-CoV-2 Tweet Activity Log
• Pandemic Social Media Discourse
• Coronavirus Tweets Analytics
• Global COVID-19 Tweet Metrics

Attributes

Original Data Source: Covid_19 Tweets Dataset

The highly contagious coronavirus (SARS-CoV-2), dubbed COVID-19 (formerly 2019-nCoV), which emerged at the close of 2019, has led to a medical emergency across the world, with the World Health Organization (WHO) officially declaring the novel coronavirus a pandemic on March 11, 2020. This report analyzes GlobalData’s social media Influencer dashboards to understand Influencer trends since the pandemic began and what key Influencers are discussing online about COVID-19. Read More

IntroductionThe worldwide COVID-19 pandemic, which began in December 2019 and has lasted for almost 3 years now, has undergone many changes and has changed public perceptions and attitudes. Various systems for predicting the progression of the pandemic have been developed to help assess the risk of COVID-19 spreading. In a case study in Japan, we attempt to determine whether the trend of emotions toward COVID-19 expressed on social media, specifically Twitter, can be used to enhance COVID-19 case prediction system performance.MethodsWe use emoji as a proxy to shallowly capture the trend in emotion expression on Twitter. Two aspects of emoji are studied: the surface trend in emoji usage by using the tweet count and the structural interaction of emoji by using an anomalous score.ResultsOur experimental results show that utilizing emoji improved system performance in the majority of evaluations.

This dataset captures the urgent pleas and global responses related to India's severe oxygen crisis during a significant wave of the COVID-19 pandemic. It reflects the critical situation where hospitals faced shortages of life-saving oxygen, beds, and anti-viral drugs, leading to mass cremations and widespread public distress. The tweets collected represent the direct voices of individuals in India requesting assistance and people worldwide appealing to their respective countries for support, particularly with oxygen supplies. It offers a unique insight into the emotional and humanitarian aspects of the crisis, serving as a record of a critical moment in the pandemic.

Columns

• user_name: The name defined by the user for their account.
• user_location: The user-defined geographical location associated with the account profile.
• user_description: A UTF-8 string provided by the user to describe their account.
• user_created: The specific time and date when the user account was established.
• user_followers: The current count of followers an account has.
• user_friends: The current count of accounts a user is following.
• user_favourites: The current number of tweets a user has marked as favourites.
• user_verified: A Boolean indicator, true if the user's account is verified.
• date: The UTC time and date when the Tweet was originally created.
• text: The actual UTF-8 text content of the Tweet.
• hashtags: All additional hashtags included in the tweet, alongside #IndiaWantsOxygen.
• source: The utility or platform used to post the Tweet (e.g., 'web' for Twitter website).
• is_retweet: Indicates whether the authenticating user has retweeted this specific Tweet.

Distribution

The dataset is structured as a collection of tweets, typically provided in a tabular format such as CSV. It consists of 25,440 tweets and is designed to be updated on a daily basis, ensuring its relevance and increasing its size over time.

Usage

This dataset is ideal for various analytical applications, including: * Exploratory Data Analysis (EDA) to uncover patterns and trends in public sentiment and crisis communication. * Natural Language Processing (NLP) for sentiment analysis, topic modelling, and understanding key themes in crisis-related social media discourse. * Data Visualisation to illustrate the geographic spread of calls for help, the volume of tweets over time, and the impact of the crisis. * Studying the societal impact of global health crises and the role of social media in humanitarian aid. * Analysing public appeals and aid coordination during emergencies.

Coverage

The dataset primarily covers tweets made with the #IndiaNeedsOxygen hashtag, focusing on the past week from its initial collection, with daily updates. The content reflects the situation across India, with people from all over the globe also participating by asking their countries to support India with oxygen tanks. The date range of tweets included generally spans from April to August 2021, with a concentration in April.

License

CC0

Who Can Use It

• Data scientists and analysts can use it to derive valuable information and insights into public health crises and humanitarian responses.
• Researchers studying social media trends, public sentiment during disasters, or the effectiveness of online advocacy.
• Non-governmental organisations (NGOs) and aid agencies to understand real-time needs and public discourse during crises.
• Students and educators for projects in data science, public health, or social studies.

Dataset Name Suggestions

• India Oxygen Crisis Tweets
• COVID-19 India Oxygen Pleas
• #IndiaNeedsOxygen Twitter Data
• India Pandemic Help Tweets
• Global Oxygen Crisis Social Data

Attributes

Original Data Source: #IndiaNeedsOxygen Tweets

Social Media in the Medical Device Market Outlook

The market size for social media in the medical device industry is experiencing significant growth, with a projected compound annual growth rate (CAGR) of 15.2% from 2024 to 2032. In 2023, the global market size was valued at approximately USD 1.5 billion, and it is expected to reach around USD 4.5 billion by 2032. Factors such as increased digital engagement, the rise of telehealth, and the growing need for patient education are driving this market's expansion.

One of the primary growth factors contributing to the market's expansion is the escalating adoption of digital health technologies. With a surge in internet penetration and the ubiquitous presence of smartphones, healthcare providers and medical device manufacturers are increasingly leveraging social media platforms to connect with patients and healthcare professionals. This digital transformation is not limited to the developed nations but has also seen substantial uptake in emerging markets, driven by the need for accessible healthcare information and services.

Another significant driver is the evolving landscape of patient engagement and education. Patients today are more proactive about their health, seeking information and peer support online. Social media platforms serve as a convenient and effective medium for disseminating educational content, promoting medical devices, and fostering communities where patients can share experiences and advice. This shift towards a more informed and engaged patient population is compelling medical device companies to enhance their social media strategies to build better relationships and trust with their audience.

The COVID-19 pandemic has further catalyzed the adoption of social media in the medical device market. With healthcare systems overwhelmed and in-person consultations limited, social media became a critical tool for real-time updates, virtual consultations, and remote monitoring. This environment accelerated the integration of social media into broader healthcare strategies, making it an indispensable component for marketing, patient support, and professional networking among healthcare providers and medical device companies.

The role of Medical Social Working Service is becoming increasingly important in the context of the digital transformation within the healthcare sector. As social media continues to expand its reach, these services are leveraging digital platforms to enhance patient support and community engagement. Medical social workers are utilizing social media to provide resources, support groups, and educational content to patients and their families. This digital engagement not only helps in addressing the psychosocial aspects of patient care but also facilitates a more holistic approach to healthcare delivery. The integration of social media in medical social work allows for real-time communication and support, making it an invaluable tool for connecting with patients and providing timely interventions.

Regionally, North America holds the largest share of the market due to the advanced healthcare infrastructure, high internet penetration, and robust adoption of digital health technologies. Europe follows closely, driven by supportive regulatory frameworks and a strong emphasis on patient-centric care. The Asia Pacific region is witnessing the fastest growth, propelled by increasing healthcare investments, rising internet usage, and a growing middle-class population demanding better healthcare services. Latin America and the Middle East & Africa are also showing positive trends, although at a comparatively slower pace due to varying degrees of digital and healthcare infrastructure development.

Platform Analysis

Facebook stands as one of the most influential platforms in the social media landscape for the medical device market. Its widespread reach allows medical device companies to engage with a broad audience, from healthcare professionals to patients. Facebook's advanced analytics and targeted advertising capabilities enable companies to tailor their marketing strategies effectively. The platform's community features, like groups and event pages, also facilitate patient education and professional networking, making it a versatile tool for the industry.

Twitter, on the other hand, excels in real-time communication and information dissemination. It is particularly valuable for live updates, whether it&#

On March 30, 2020, the Mexican government declared the state of emergency following the coronavirus (COVID-19) outbreak in the country, calling for the immediate suspension of non-essential activities. With people staying home, the popularity of internet shopping became quickly evident, notably online purchases of consumer goods and food. This trend could be seen in social media, where hashtags related to food delivery took on particular force. The most used hashtag of this kind in the country was #rappi, which was mentioned over 22,000 times from May 19 to June 17, 2020.

Background: COVID-related misinformation is prevalent online, including on social media. The purpose of this study was to explore factors associated with user engagement with COVID-related misinformation on the social media platform, TikTok.

Methods: A sample of TikTok videos associated with the hashtag #coronavirus were downloaded on September 20, 2020. Misinformation was evaluated on a scale (low, medium, high) using a codebook developed by experts in infectious diseases. Multivariable modeling was used to evaluate factors associated with number of views and presence of user comments indicating intention to change behavior.

Results: 166 TikTok videos were identified. Moderate misinformation was present in 36 (22%) videos, and high-level misinformation was present in 11 (7%). After controlling for characteristics and content, videos containing moderate misinformation were less likely to generate a user response indicating intended behavior change. By contrast, videos containing high-level misinformation were less likely to be viewed but demonstrated a non-significant trend towards higher engagement among viewers.

Conclusions: COVID-related misinformation is less frequently viewed on TikTok but more likely to engage viewers. Public health authorities can combat misinformation on social media by posting content of their own.

TikTok saw an unprecedented increase in popularity during the coronavirus (COVID-19) outbreak in the Nordic region. The largest increase, of up to *** percent was observed among Danish youth. While *** percent of them used TikTik before the COVID-19 outbreak, the corresponding share during the pandemic was ** percent. Overall, TikTok became more popular in Denmark, Sweden, Norway and Finland during the pandemic, regardless of the users’ age.

The rise of TikTok   

TikTok is a Chinese video-sharing social network, initially released in 2018, as Musical.ly. Over the period from 2017 to 2020, the app generated increasingly larger engagement rates, reaching nearly ** million daily active users via iOS as of May 2020 on a global scale. Among the most followed accounts in Norway were the pop duo Marcus & Martinus.

COVID-19 on social media   

As of March 2020, almost all the most popular hashtags on social media in Sweden were related to the coronavirus. In fact, a recent survey showed that especially younger individuals worldwide seemed to rely on social media for updates on the coronavirus that same month . In contrast, the figures were much lower for people aged 55 or older. Nevertheless, social media use generally increased during the pandemic and facilitated the spread of news regarding the coronavirus. In some cases, even false information.

Social media is one of the go-to news sources in the United States – over one third of U.S. adults responding to a 2022 survey got their news from social media platforms every day, and a further 22 percent did so a few times or at least once per week. After the surge in social media news consumption in 2020 at the height of the COVID-19 pandemic, daily engagement fell in 2021, but the increase the following year suggests that daily news access on social networks could continue to grow in years to come.

The most popular social sites for news

An annual report surveying U.S. adults from 2019 to 2022 revealed that Facebook was the most popular social network used for news, followed by YouTube. Important to note here though is that TikTok was not included in the survey question for those years, a platform increasingly popular with younger generations. Whilst the share of adults regularly using TikTok for news aged 50 years or above was just five percent, among those aged between 18 and 29 years the figure was over five times higher.

Meanwhile, Twitter is journalists’ preferred social media site, with the share who use Twitter for their job at almost 70 percent. Since Elon Musk’s takeover of Twitter however, some journalists raised concerns about the future of free speech on the platform.

Gen Z and social media news consumption

A 2022 survey found that half of all Gen Z respondents used social media for news every day. Gen Z is driving growth in social media news usage, a trend which will continue if the younger consumers belonging to this generation increase their engagement with news as they age.

The report provides a snapshot of the social media usage trends amongst online Canadian adults based on an online survey of 1500 participants. Canada continues to be one of the most connected countries in the world. An overwhelming majority of online Canadian adults (94%) have an account on at least one social media platform. However, the 2022 survey results show that the COVID-19 pandemic has ushered in some changes in how and where Canadians are spending their time on social media. Dominant platforms such as Facebook, messaging apps and YouTube are still on top but are losing ground to newer platforms such as TikTok and more niche platforms such as Reddit and Twitch.

This dataset captures Twitter activity related to Covid-19, focusing on the initial phase of the pandemic from April to June 2020 [1, 2]. It comprises 235,240 worldwide tweets in English, streamed live at a rate of approximately 10,000 tweets per day after the World Health Organisation declared Covid-19 a pandemic [1, 2]. The tweets were collected using relevant hashtags such as #covid-19, #coronavirus, #covid, #covaccine, #lockdown, #homequarantine, #quarantinecenter, #socialdistancing, #stayhome, and #staysafe [1, 2].

The data has undergone pre-processing, which involved converting all tweets to lowercase, removing extra white spaces, numbers, special characters, ASCII characters, URLs, punctuations, and stopwords [2]. Additionally, all instances of 'covid' were converted to 'covid19', and stemming was applied to reduce inflected words to their root forms [2]. Sentiment analysis has been performed on each cleaned tweet using an NLTK-based Sentiment Analyser, providing sentiment scores for positive, negative, and neutral categories, and a compound sentiment score [2]. Tweets are classified as Positive, Negative, or Neutral based on these scores [2].

Columns

• id: Unique identifier for the tweet [1].
• Tweet ID: Unique identifier for the tweet [2]. (Note: Appears to be the same as 'id')
• created_at: The date and time when the tweet was created [1].
• Creation Date & Time: The date and time when the tweet was created [2]. (Note: Appears to be the same as 'created_at')
• source: The source link from which the tweet was posted [1].
• Source Link: The source link from which the tweet was posted [2]. (Note: Appears to be the same as 'source')
• original_text: The full text of the original tweet [1].
• Original Tweet: The full text of the original tweet [2]. (Note: Appears to be the same as 'original_text')
• lang: The language of the tweet [1].
• favorite_count: The number of times the tweet was favourited [1].
• Favorite Count: The number of times the tweet was favourited [2]. (Note: Appears to be the same as 'favorite_count')
• retweet_count: The number of times the tweet was retweeted [1].
• Retweet Count: The number of times the tweet was retweeted [2]. (Note: Appears to be the same as 'retweet_count')
• original_author: The original author of the tweet [3].
• Original Author: The original author of the tweet [2]. (Note: Appears to be the same as 'original_author')
• hashtags: Hashtags included in the tweet [3].
• Hashtags: Hashtags included in the tweet [2]. (Note: Appears to be the same as 'hashtags')
• user_mentions: User mentions within the tweet [3].
• User Mentions: User mentions within the tweet [2]. (Note: Appears to be the same as 'user_mentions')
• Place: Location associated with the tweet [2].

Distribution

The dataset consists of 235,240 tweets from the first phase of collection [1, 2]. Data files are typically provided in CSV format [4]. The tweets were collected from 19th April to 20th June 2020 [1].

Usage

This dataset is ideal for various data science and analytics applications, including Natural Language Processing (NLP), Deep Learning, Text Classification, and Ensembling [2]. Its pre-processed nature and included sentiment scores make it particularly useful for sentiment analysis research related to public opinion during the Covid-19 pandemic [2].

Coverage

The dataset covers a time range from 19th April to 20th June 2020 [1]. It includes worldwide tweets [2] and is limited to English language content [2]. Tweet sources are primarily Twitter for Android (31%) and Twitter for iPhone (28%), with 41% originating from other sources [5].

License

CC-BY-SA

Who Can Use It

• Data Scientists and Analysts: For conducting social media analysis, trend identification, and public sentiment tracking during the pandemic [2].
• Researchers in NLP and Machine Learning: To train and evaluate text classification models, conduct deep learning experiments, and explore ensembling techniques [2].
• Public Health Researchers: To understand public response, concerns, and sentiment towards Covid-19, lockdowns, and vaccines [2].
• Academics and Students: For academic projects, dissertations, and learning about real-world social media data analysis and sentiment classification [2].

Dataset Name Suggestions

• COVID-19 Twitter Sentiment (Apr-Jun 2020)
• Pandemic Twitter Activity Dataset (Phase 1)
• Global Covid-19 Tweets with Sentiment Analysis
• Social Media Response to Covid-19: April-June 2020
• Twitter Covid-19 Discourse (Early Pandemic)

Attributes

Original Data Source: Covid-19 Twitter Dataset

COVID-19 is a highly contagious and novel virus that has prompted government officials to implement restrictive public health orders. It is hypothesized that pandemic-related restrictions may have a detrimental impact on mental health. Longitudinal data were collected through 13 assessments, repeated every two weeks for the initial six months of the COVID-19 pandemic. Participants were recruited through Athabasca University and social media . The final sample consisted of 280 adults from across Canada , with the majority of participants residing in Alberta (63%) and Ontario (20%) . Sociodemographic characteristics, COVID-19 related risk factors, pre-pandemic and pandemic physical activity, and COVID-19 related risk factors were collected at study entry, and mental health (depressive symptoms, anxiety, and loneliness) were collected at each assessment. Multi-level modelling was used to identify mental health trajectories during the initial six months of the pandemic. Mental health symptoms tracked with rising cases of infection and subsequent public health restrictions during the pandemic. Specifically, anxiety and depressive symptoms demonstrated strong longitudinal quadratic trends. Both anxiety and depressive symptoms were high at study entry (May 2020) and decreased over the summer, followed by an increase in the fall and winter months. Loneliness was stable over the follow-up period. Age, sex, living alone, socioeconomic factors, and pre-existing mental health conditions correlated with mental health symptoms during the pandemic's initial six months. This study characterizes within-person changes to mental health (anxiety, depressive symptoms, and loneliness) in a Canadian sample from May 2020 to January 2021 during the COVID-19 pandemic.

Metaverse Social Media Platforms Market Outlook

The global metaverse social media platforms market size was valued at approximately $69.9 billion in 2023 and is projected to grow to a massive $1,527.4 billion by 2032, reflecting a staggering CAGR of 41.9% over the forecast period. The remarkable growth of this market is primarily driven by the increasing adoption of advanced technologies such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), coupled with the rising demand for immersive social experiences. The continued technological advancements in these areas, along with the proliferation of high-speed internet and growing investments by leading tech companies, are expected to further fuel market expansion over the coming years.

One of the key growth factors of the metaverse social media platforms market is the technological advancements that have led to the development of more sophisticated and accessible VR and AR devices. These technologies have significantly improved user experiences by providing immersive and interactive environments that replicate real-world social interactions. Companies are heavily investing in R&D to innovate and launch new products and services that cater to the ever-evolving needs of consumers. This has created a fertile ground for the adoption of metaverse platforms, especially among the tech-savvy younger generation.

Another critical factor propelling the market's growth is the increasing consumer inclination towards digital and virtual spaces due to the COVID-19 pandemic. The pandemic has accelerated the shift towards remote interactions, with people seeking alternative ways to socialize, work, and entertain themselves. As a result, there has been a substantial rise in the adoption of metaverse social media platforms, which offer unique and engaging ways to connect with others virtually. This trend is expected to continue post-pandemic, as people have grown accustomed to the convenience and novelty of virtual interactions.

Furthermore, the growing popularity of virtual events and online gaming is significantly contributing to the market's expansion. Metaverse platforms are increasingly being used to host virtual concerts, conferences, and other events, providing users with novel and immersive experiences. The gaming industry, in particular, has been quick to adopt metaverse technologies, with numerous game developers integrating social features into their games to enhance user engagement and retention. This convergence of gaming and social media is creating a vibrant ecosystem that is poised for substantial growth.

From a regional perspective, North America currently holds the largest share of the metaverse social media platforms market, driven by the presence of major tech giants and high consumer adoption rates. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, owing to the rapid technological advancements, increasing internet penetration, and growing investments in the region. Europe and Latin America are also anticipated to experience steady growth, supported by the rising demand for innovative social media platforms and growing consumer awareness.

The Metaverse In Entertainment is revolutionizing how audiences engage with content, offering immersive experiences that transcend traditional media boundaries. As entertainment companies explore the metaverse, they are creating virtual worlds where users can interact with their favorite shows, movies, and music in unprecedented ways. This new frontier allows for personalized experiences, where fans can become part of the story, attend virtual concerts, or explore digital theme parks. The integration of entertainment into the metaverse is not only enhancing user engagement but also opening up new revenue streams through virtual merchandise and exclusive content offerings. As the technology continues to evolve, the potential for storytelling and audience interaction in the metaverse is limitless, promising a future where entertainment is more interactive and immersive than ever before.

Platform Type Analysis

The metaverse social media platforms market can be segmented by platform type into Virtual Reality (VR) Platforms, Augmented Reality (AR) Platforms, and Mixed Reality (MR) Platforms. VR platforms have been increasingly adopted for their ability t

Higher 95% PI coverage and lower RMSE, MAE, WIS and MIS represent better performance. Best performing model is given in bold with the superscript “a”.

According to Cognitive Market Research, The Global Trend brand market size in 2023 was XX USD billion and will grow at a compound annual growth rate (CAGR) of 5.6% from 2023 to 2030.

The demand for trend brands is rising due to economic factors, disposable income, supply chain efficiency, and competition and brand differentiation.
Demand for below 22 L remains higher in the trend brand market.
The residential segment held the highest trend brand market revenue share in 2023.
North America will continue to lead, whereas the Asia Pacific trend brand market will experience the strongest growth until 2030.

Changes in Consumer Tastes and Lifestyle Choices to Direct Market Growth

The trend brand market is heavily influenced by basic forces such as changes in consumer tastes and lifestyle choices. These factors mostly determine the growth or collapse of the industry. Customer preferences are constantly changing due to a variety of causes, including socioeconomic trends, generational variations, and cultural developments. For trend brands to be relevant, they need to keep up with these changes.

For example, Gen Z and Millennials are very interested in ethical and sustainable products. The increasing demand for environmentally friendly apparel has resulted in trend brands incorporating sustainable practices into their production procedures. Furthermore, the emergence of influencer culture and social media has expedited trends, necessitating swift brand adaptation in order to maintain competitiveness. The COVID-19 epidemic further modified consumer tastes. A noticeable trend toward loungewear and comfy clothing was observed as more people worked from home. Trending brands had to modify their lineups to satisfy the growing consumer desire for comfort without compromising style.

Innovations in Technology to Indicate Market Growth

Innovations in technology have a significant influence on the trend brand market. These developments affect many facets of the sector, including marketing plans and production procedures. The way trend brands create and manufacture their goods has changed dramatically as a result of the use of new production technologies like automation and 3D printing. Increased customization, accuracy, and quicker production cycles are all made possible by it. This lowers expenses while also allowing firms to provide distinctive, limited-edition products, appealing to consumers by giving them a sense of exclusivity.

The emergence of digital platforms and e-commerce has revolutionized the way trend brands interact with their target customers in the marketing domain. In particular, social media is an effective tool for interacting with customers and promoting brands. Companies may use data analytics to improve their understanding of consumer behavior, target marketing campaigns, and enhance their product offers by using real-time feedback. The virtual reality (VR) and augmented reality (AR) technologies are also improving the online buying experience. Virtual try-on capabilities for apparel and accessories help customers feel more confident about their selections and alleviate some of the negative aspects of online buying.

Market Dynamics of the Trend brand

Variations in Consumer Spending to Hinder Market Growth

Consumer spending is directly impacted during times of global financial crisis or economic recession. Consumer discretionary spending tends to fall during economic downturns, which can be detrimental to trend brands that depend on disposable money and consumer confidence. A spike in inflation can result in greater manufacturing costs, which are then frequently transferred to customers as higher pricing. Customers may become less able to afford items from trend brands as a result, which may cause them to be pickier about what they buy. Trend brands are susceptible to currency swings if they source materials or products from other countries. Variability in exchange rates can have an impact on manufacturing costs, which may lead to lower profit margins or the need to modify prices, both of which can have an impact on sales.

Impact of COVID-19 on the Trend Brand Market

The COVID-19 pandemic has significantly impacted the market for trend brands. Due to economic uncertainty, it first resulted in lower consumer spending, which affected industry sales. However, as more people started shopping online, e-commerce became more popular....

Time Series Data Analysis for the paper "Less reliable media drive interest in anti-vaccine information": This file contains the time series analysis (ADF test for stationarity, fitting of VAR model, granger causality tests, IRF plots) on the final time-series data used in the paper. The VAR model uses the data at levels. We run this analysis for Antivaxx terms across different platforms, and media outlets. The google trends data variable was generated using google trends and is restricted to queries from the US. For further questions, please reach out to the authors at ss5910@columbia.edu.

Higher 95% PI coverage and lower RMSE, MAE, WIS and MIS represent better performance. Best performing model is given in bold with the superscript "a”.

This dataset contains tweets about vaccination in Indonesia, collected using the hashtags #vaksin and #vaksinasi. The data collection commenced on 1st November 2020. This dataset is valuable for evaluating sentiments and understanding public discourse surrounding vaccination efforts in Indonesia.

Columns

The dataset includes the following columns: * id: Unique identifier for the tweet. * date: Date the tweet was posted. * text: The full text content of the tweet. * hashtags: Hashtags used in the tweet. * user_name: The username of the tweet author. * user_location: The stated location of the tweet author. * user_description: The profile description of the tweet author. * user_created: The date the user's account was created. * user_followers: The number of followers the user has. * user_friends: The number of accounts the user is following.

Distribution

The dataset is typically provided in CSV format. It includes 13,467 unique tweet records covering the period from 10th January 2021 to 21st April 2021. The dataset structure allows for analysis of tweet content, user demographics, and temporal trends in vaccination discourse. Detailed counts are available for various 5-day intervals within this sample period.

Usage

This dataset is ideally suited for: * Sentiment analysis of public opinion on vaccination. * Analysing social media discourse related to public health campaigns. * Trend analysis of vaccination-related discussions over time. * Research into public health communication strategies. * Developing and testing Natural Language Processing (NLP) models.

Coverage

• Geographic Scope: The dataset focuses on tweets originating from Indonesia.
• Time Range: Data collection began on 1st November 2020. The provided sample covers tweets from 10th January 2021 to 21st April 2021. User account creation dates within the dataset range from 10th November 2007 to 15th April 2021.
• Demographic Scope: The dataset captures general public discourse on Twitter in Indonesia. Specific demographic details beyond user location and account creation dates are not explicitly provided.

License

CCO

Who Can Use It

This dataset is intended for: * Researchers studying public health, social media trends, and sentiment analysis. * Data scientists and machine learning engineers working on NLP tasks. * Policy makers and public health organisations seeking insights into public perception and engagement. * Academics and students for educational purposes and social science research.

Dataset Name Suggestions

• Indonesian Vaccination Tweets
• Indonesian COVID-19 Vaccination Discourse
• Indonesia Twitter Vaccine Sentiment
• Social Media Discourse on Vaccination in Indonesia
• Indonesian Public Opinion on Vaccines

Attributes

Original Data Source: Indonesian Vaccination Tweets