As of February 2024, over a third of online users worldwide were aged between 25 and 34 years. Website visitors in this age bracket constituted the biggest group of online users worldwide. Also, 19 percent of global online users were aged 18 to 24 years. The global digital population aged 65 or older represented approximately 4.2 percent of all internet users worldwide. Social media usage and Meta Social media is a major driver of internet use, with a global penetration rate of 62.2 percent. On average, internet users spend 143 minutes per day on social media, highlighting its significant impact on daily online activities. The usage of social media is mostly dominated by Meta platforms, which own four of the largest social media platforms. Facebook leads the ranking with over three billion active users, followed by Instagram and WhatsApp. Instagram's global popularity Meta’s social video platform, Instagram, had long been one of the most engaging social media platforms worldwide, and it was projected to reach 1.44 billion monthly active users. Instagram was particularly favored by users aged 18 to 34, thanks to its ability to offer a variety of interactive content, from images and carousels. This diverse range of content types was a key factor in its popularity among its young user base.

Social media platforms are integral to people's lives, offering ways to communicate, create and view content and share information. According to Ofcom, approximately 89% of UK internet users in 2023 used social media apps or sites. Teenagers and young adults are the biggest users, although there is rapid uptake among older age groups. Advertising is the primary revenue source for social media platforms, although subscription-based services are gaining momentum as platforms seek to diversify their incomes. TikTok is the success story of the last few years, becoming the most downloaded app between 2020 and 2022, according to Apptopia. The short-form video platform reported that it averaged revenue growth of over 450% between 2019 and 2022. After Musk's takeover, X, formerly known as Twitter, adjusted its content moderation and allowed previously banned accounts to return. As a result, over 600 advertisers have pulled their ads from the site because of fears their brand may be associated with malcontent. In response to falling ad revenue, X has introduced a subscription-based service which enables users to verify themselves and boosts the number of people who view their tweets. Meta-owned Facebook and Instagram have responded by introducing a similar service. Revenue is expected to grow by 14.3% in 2024-25, constrained by a slowdown in user growth for most major social media platforms. Over the five years through 2024-25, revenue is forecast to expand at a compound annual rate of 32.8% to reach £9.8 billion. Looking forward, regulations relating to how data is collected, stored, and shared will force advertisers and platforms to rethink how they can target their desired demographics. The rising prominence of AI will require the introduction of adequate regulations. The Online Safety Bill sets out new guidelines for social media platforms to abide by, with hefty fines in store for those who do not. Operating costs will swell as platforms look to meet consumers’ expectations, weighing on profit. Over the five years through 2029-30, social media platforms' revenue is projected to climb at an estimated 9.4% to reach £15.4 billion.

How many people use social media? Social media usage is one of the most popular online activities. In 2024, over **** ******* people were using social media worldwide, a number projected to increase to over *** billion in 2029. Who uses social media? Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at ** percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe. How much time do people spend on social media? Social media is an integral part of daily internet usage. On average, internet users spend *** minutes per day on social media and messaging apps, an increase of ** minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media. What are the most popular social media platforms? Market leader Facebook was the first social network to surpass *** billion registered accounts and currently boasts approximately *** billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Introduction

Video Marketing Statistics: Video marketing has emerged as a cornerstone of digital strategies, leveraging the power of visual content to engage audiences and drive business growth. As video consumption continues to rise, companies across various industries increasingly adopt video as a critical component of their marketing efforts. The statistics surrounding video marketing reflect its growing influence, with metrics highlighting the effectiveness of different video formats, audience preferences, and the impact on consumer behavior.

From how-to guides to thought leadership pieces, video marketing is an essential tool for brands looking to connect more dynamically and impactfully with their target audiences.

Overview
The Controllable Multimodal Feedback Synthesis (CMFeed) Dataset is designed to enable the generation of sentiment-controlled feedback from multimodal inputs, including text and images. This dataset can be used to train feedback synthesis models in both uncontrolled and sentiment-controlled manners. Serving a crucial role in advancing research, the CMFeed dataset supports the development of human-like feedback synthesis, a novel task defined by the dataset's authors. Additionally, the corresponding feedback synthesis models and benchmark results are presented in the associated code and research publication.

Task Uniqueness: The task of controllable multimodal feedback synthesis is unique, distinct from LLMs and tasks like VisDial, and not addressed by multi-modal LLMs. LLMs often exhibit errors and hallucinations, as evidenced by their auto-regressive and black-box nature, which can obscure the influence of different modalities on the generated responses [Ref1; Ref2]. Our approach includes an interpretability mechanism, as detailed in the supplementary material of the corresponding research publication, demonstrating how metadata and multimodal features shape responses and learn sentiments. This controllability and interpretability aim to inspire new methodologies in related fields.

Data Collection and Annotation
Data was collected by crawling Facebook posts from major news outlets, adhering to ethical and legal standards. The comments were annotated using four sentiment analysis models: FLAIR, SentimentR, RoBERTa, and DistilBERT. Facebook was chosen for dataset construction because of the following factors:
• Facebook was chosen for data collection because it uniquely provides metadata such as news article link, post shares, post reaction, comment like, comment rank, comment reaction rank, and relevance scores, not available on other platforms.
• Facebook is the most used social media platform, with 3.07 billion monthly users, compared to 550 million Twitter and 500 million Reddit users. [Ref]
• Facebook is popular across all age groups (18-29, 30-49, 50-64, 65+), with at least 58% usage, compared to 6% for Twitter and 3% for Reddit. [Ref]. Trends are similar for gender, race, ethnicity, income, education, community, and political affiliation [Ref]
• The male-to-female user ratio on Facebook is 56.3% to 43.7%; on Twitter, it's 66.72% to 23.28%; Reddit does not report this data. [Ref]

Filtering Process: To ensure high-quality and reliable data, the dataset underwent two levels of filtering:
a) Model Agreement Filtering: Retained only comments where at least three out of the four models agreed on the sentiment.
b) Probability Range Safety Margin: Comments with a sentiment probability between 0.49 and 0.51, indicating low confidence in sentiment classification, were excluded.
After filtering, 4,512 samples were marked as XX. Though these samples have been released for the reader's understanding, they were not used in training the feedback synthesis model proposed in the corresponding research paper.

Dataset Description
• Total Samples: 61,734
• Total Samples Annotated: 57,222 after filtering.
• Total Posts: 3,646
• Average Likes per Post: 65.1
• Average Likes per Comment: 10.5
• Average Length of News Text: 655 words
• Average Number of Images per Post: 3.7

Components of the Dataset
The dataset comprises two main components:
• CMFeed.csv File: Contains metadata, comment, and reaction details related to each post.
• Images Folder: Contains folders with images corresponding to each post.

Data Format and Fields of the CSV File
The dataset is structured in CMFeed.csv file along with corresponding images in related folders. This CSV file includes the following fields:
• Id: Unique identifier
• Post: The heading of the news article.
• News_text: The text of the news article.
• News_link: URL link to the original news article.
• News_Images: A path to the folder containing images related to the post.
• Post_shares: Number of times the post has been shared.
• Post_reaction: A JSON object capturing reactions (like, love, etc.) to the post and their counts.
• Comment: Text of the user comment.
• Comment_like: Number of likes on the comment.
• Comment_reaction_rank: A JSON object detailing the type and count of reactions the comment received.
• Comment_link: URL link to the original comment on Facebook.
• Comment_rank: Rank of the comment based on engagement and relevance.
• Score: Sentiment score computed based on the consensus of sentiment analysis models.
• Agreement: Indicates the consensus level among the sentiment models, ranging from -4 (all negative) to 4 (all positive). 3 negative and 1 positive will result into -2 and 3 positives and 1 negative will result into +2.
• Sentiment_class: Categorizes the sentiment of the comment into 1 (positive) or 0 (negative).

More Considerations During Dataset Construction
We thoroughly considered issues such as the choice of social media platform for data collection, bias and generalizability of the data, selection of news handles/websites, ethical protocols, privacy and potential misuse before beginning data collection. While achieving completely unbiased and fair data is unattainable, we endeavored to minimize biases and ensure as much generalizability as possible. Building on these considerations, we made the following decisions about data sources and handling to ensure the integrity and utility of the dataset:

• Why not merge data from different social media platforms? We chose not to merge data from platforms such as Reddit and Twitter with Facebook due to the lack of comprehensive metadata, clear ethical guidelines, and control mechanisms—such as who can comment and whether users' anonymity is maintained—on these platforms other than Facebook. These factors are critical for our analysis. Our focus on Facebook alone was crucial to ensure consistency in data quality and format.

• Choice of four news handles: We selected four news handles—BBC News, Sky News, Fox News, and NY Daily News—to ensure diversity and comprehensive regional coverage. These news outlets were chosen for their distinct regional focuses and editorial perspectives: BBC News is known for its global coverage with a centrist view, Sky News offers geographically targeted and politically varied content learning center/right in the UK/EU/US, Fox News is recognized for its right-leaning content in the US, and NY Daily News provides left-leaning coverage in New York. Many other news handles such as NDTV, The Hindu, Xinhua, and SCMP are also large-scale but may contain information in regional languages such as Indian and Chinese, hence, they have not been selected. This selection ensures a broad spectrum of political discourse and audience engagement.

• Dataset Generalizability and Bias: With 3.07 billion of the total 5 billion social media users, the extensive user base of Facebook, reflective of broader social media engagement patterns, ensures that the insights gained are applicable across various platforms, reducing bias and strengthening the generalizability of our findings. Additionally, the geographic and political diversity of these news sources, ranging from local (NY Daily News) to international (BBC News), and spanning political spectra from left (NY Daily News) to right (Fox News), ensures a balanced representation of global and political viewpoints in our dataset. This approach not only mitigates regional and ideological biases but also enriches the dataset with a wide array of perspectives, further solidifying the robustness and applicability of our research.

• Dataset size and diversity: Facebook prohibits the automatic scraping of its users' personal data. In compliance with this policy, we manually scraped publicly available data. This labor-intensive process requiring around 800 hours of manual effort, limited our data volume but allowed for precise selection. We followed ethical protocols for scraping Facebook data , selecting 1000 posts from each of the four news handles to enhance diversity and reduce bias. Initially, 4000 posts were collected; after preprocessing (detailed in Section 3.1), 3646 posts remained. We then processed all associated comments, resulting in a total of 61734 comments. This manual method ensures adherence to Facebook’s policies and the integrity of our dataset.

Ethical considerations, data privacy and misuse prevention
The data collection adheres to Facebook’s ethical guidelines [<a href="https://developers.facebook.com/terms/"

Social media users were the highest across India as of February 2021 for messaging app Whatsapp, at *** million. YouTube followed during the same time, with Facebook ranking third. The government in the country introduced new rules under its IT-law earlier that year with the aim of social media platforms self-regulating by focusing on stronger guidelines.

Companion DATA

Title: Using social media and personality traits to assess software developers' emotional polarity

Authors: Leo Moreira Silva Marília Gurgel Castro Miriam Bernardino Silva Milena Santos Uirá Kulesza Margarida Lima Henrique Madeira

Journal: PeerJ Computer Science

Github: https://github.com/leosilva/peerj_computer_science_2022

The folders contain:

Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.

/analysis analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications

/dataset alldata.json: contains the dataset used in the paper

/ethics_committee committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version. committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version. committee_submission_form_english_version.pdf: the project submitted to the committee. English version. committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version. consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version. consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version. data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version. data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.

/notebooks General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period

/surveys Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version. Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version. Demographic_Survey_answers.xlsx: participants' demographic survey answers ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits. ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits. ibf_answers.xlsx: participantes' and psychologists' answers for BFI

We have removed from dataset any sensible data to protect participants' privacy and anonymity. We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.

Social Media Market Outlook

According to our latest research, the global social media market size reached a substantial valuation of USD 244.2 billion in 2024. The industry is experiencing robust expansion, registering a compound annual growth rate (CAGR) of 12.4% from 2025 to 2033. This vigorous growth is primarily attributed to the increasing penetration of internet-enabled devices, rapid digital transformation across sectors, and the evolving role of social media as a primary channel for communication and commerce. By 2033, the market is forecasted to reach an impressive USD 697.3 billion, underscoring the profound influence and integration of social media platforms in both personal and professional spheres. These figures reflect the dynamic evolution and the critical role social media plays in shaping global digital interactions and marketing strategies.

One of the most significant growth factors in the social media market is the exponential rise in global internet and smartphone penetration. As of 2024, over 5.3 billion people worldwide are active internet users, with approximately 4.8 billion engaging regularly on social media platforms. The widespread availability of affordable smartphones and high-speed internet has democratized access to digital content and social networking, facilitating seamless connectivity and real-time interactions. This digital proliferation is particularly evident in emerging economies, where first-time internet users are driving a surge in new social media accounts and engagement. The increasing reliance on mobile devices for social networking, content sharing, and digital commerce has further fueled market expansion, making social media an indispensable aspect of daily life for billions.

Another pivotal driver propelling the social media market is the transformation of these platforms into versatile business tools. Enterprises across industries are leveraging social media for brand building, customer engagement, targeted advertising, and data-driven insights. The integration of advanced analytics, artificial intelligence, and machine learning algorithms has enabled brands to deliver personalized experiences, optimize marketing campaigns, and measure consumer sentiment in real-time. Moreover, the rise of social commerce and influencer marketing has opened new revenue streams, with platforms such as Instagram, TikTok, and Facebook pioneering innovative commerce models that blend content, community, and commerce. These developments have attracted significant investments from advertisers and marketers, further accelerating market growth and platform diversification.

The evolving regulatory landscape and the growing emphasis on data privacy and content moderation have also shaped the trajectory of the social media market. Governments and regulatory bodies worldwide are introducing stricter guidelines to address issues such as misinformation, data breaches, and harmful content. While these regulations present compliance challenges, they also create opportunities for platforms to build trust and enhance user safety through transparent policies and advanced security measures. The adoption of robust content moderation tools, user verification systems, and privacy-centric features is fostering a safer and more reliable social media environment. These efforts are crucial for sustaining user engagement and attracting new demographics, particularly as concerns over digital well-being and online safety gain prominence.

Regionally, the Asia Pacific market has emerged as the largest and fastest-growing segment, accounting for over 38% of the global social media market in 2024. This growth is driven by the massive population base, rapid urbanization, and the proliferation of affordable mobile devices in countries such as China, India, and Indonesia. North America remains a mature market, characterized by high adoption rates, advanced technological infrastructure, and significant advertising spend. Europe follows closely, with increasing regulatory oversight and a strong focus on data privacy and digital innovation. Meanwhile, Latin America and the Middle East & Africa are witnessing accelerated growth, fueled by rising internet penetration and the increasing adoption of digital platforms among younger demographics. Each region presents unique opportunities and challenges, shaping the competitive dynamics and strategic priorities of key market players.

Cloud Social Media Management Market Outlook

The global cloud social media management market size was valued at approximately USD 8.5 billion in 2023 and is projected to reach an impressive USD 26.3 billion by 2032, growing at a compelling CAGR of 13.5% during the forecast period. This robust growth is driven by the increasing adoption of cloud-based solutions among enterprises aiming to enhance their social media strategies and customer engagement efforts.

One of the primary growth factors of the cloud social media management market is the rising penetration of social media platforms across different demographics globally. Businesses are increasingly recognizing the importance of a strong social media presence as a crucial element of their marketing strategies. This has led to a surge in demand for sophisticated tools that facilitate efficient management of social media activities, including content creation, distribution, and analytics, all of which are seamlessly integrated through cloud solutions.

Another contributing factor is the advancements in artificial intelligence (AI) and machine learning (ML). These technologies are being integrated into cloud social media management tools to offer advanced analytics, sentiment analysis, and predictive analytics capabilities. By leveraging AI and ML, companies can gain deeper insights into customer behavior, preferences, and trends, enabling them to craft more targeted and effective social media campaigns. This technological progression is a significant driver of market growth.

Moreover, the proliferation of mobile devices and the increasing accessibility of high-speed internet have also played a vital role. More consumers are accessing social media platforms via their smartphones, leading businesses to seek out cloud-based solutions that offer mobile compatibility and real-time updates. This trend necessitates the need for flexible and scalable cloud-based social media management tools that can cater to the dynamic nature of social media interactions.

Social Media Integration is a crucial aspect of cloud social media management, enabling businesses to unify their social media efforts across various platforms. By integrating social media channels, companies can streamline their communication strategies, ensuring consistent messaging and branding. This integration allows for a more cohesive approach to managing social media interactions, facilitating better engagement with audiences. Moreover, integrated platforms provide a centralized dashboard for monitoring and analyzing social media performance, making it easier for businesses to track their campaigns' effectiveness. As social media continues to evolve, the ability to integrate these platforms into a single, manageable system becomes increasingly important for maintaining a competitive edge.

Regionally, North America stands out as a significant market for cloud social media management solutions, driven by the presence of major technology companies and a high rate of social media adoption. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, supported by the rapid digital transformation in countries like China and India, increasing internet penetration, and a burgeoning young population active on social media platforms.

Component Analysis

In the cloud social media management market, the component segment is primarily divided into software and services. The software segment includes various tools and platforms designed for social media management, such as scheduling tools, content creation tools, and analytics platforms. The services segment encompasses implementation, consulting, and support services essential for leveraging these software tools effectively.

Within the software segment, content creation tools have seen significant demand as they enable businesses to generate engaging and creative content that resonates with their target audience. Scheduling tools are also crucial as they help in planning and automating the posting process, ensuring consistent online presence without manual intervention. Analytics platforms are indispensable for measuring the performance of social media campaigns, providing insights into key metrics like reach, engagement, and ROI.

The services segment, on the other hand, is driven by the need for professional expertise in deploying and managing social media tools.

Introduction

Digital marketing Statistics: Digital marketing is a process used by global businesses that allows promoting products and services using the internet and digital devices. This is an important component of marketing in almost every business, which allows them to reach their customers. This marketing segment basically includes computers, smartphones, social media, other online platforms, emails, search engines, and mobile apps. Digital marketing helps brands to grow enormously and globally by connecting with the right audience at the right time.

This article explores all current trends of digital marketing from different insights that will guide you in understanding the topic effectively. Digital Marketing Statistics mainly cover the overall spending, social media marketing, mobile marketing, email marketing, SEO & content marketing, influencer marketing, B2B vs. B2C marketing stats, and many other factors.

Introduction

Discord vs Telegram Statistics: Discord and Telegram are two of the most popular communication platforms today, each serving different audiences with unique features, strengths, and weaknesses. Discord is an instant messaging and VoIP social platform that allows communication through voice calls, video calls, text messaging, and media sharing. In contrast, Telegram, also known as Telegram Messenger, is a cloud-based, cross-platform messaging service that combines social media and instant messaging (IM).

This article will guide you effectively, as it provides a comprehensive comparison between the two platforms, including features, pricing, user experience, and performance, to help determine which platform best suits community-building tools or fast, secure messaging in 2025.

The researcher followed an interpretivist research paradigm and applied the qualitative research approach. The type of research was applied research. The study had an exploratory and a descriptive purpose as the researcher explored the participants’ experiences of social media use to gain a better understanding of the phenomenon, and subsequently provided a description of the phenomenon. The study was planned an implemented according to a case study design. The instrumental case study was specifically implemented as the goal of the study was to obtain knowledge and insight into a specific phenomenon. The study population consisted of young adults who used social media during their adolescent years and being a qualitative study, a non-probability sampling method was used. Data was collected by means of semi-structured interviews, in which an interview schedule or guide with open-ended questions were used to guide the interview and probe the participants’ answers. The participants provided their permission for the researcher to digitally record the interviews. Data was analysed by means of following the thematic analysis steps. The researcher attended to the aspect of data quality by considering the elements of credibility, transferability, dependability and confirmability. The research was conducted with consideration of ethical research principles such as avoidance of harm, voluntary participation and informed consent, confidentiality, no deception and debriefing of participants.

Demographic characteristics of expert panel members.

The restaurant guide app market is experiencing robust growth, driven by the increasing penetration of smartphones, rising demand for convenient dining options, and the growing popularity of online food delivery services. The market's expansion is fueled by several key trends, including the integration of advanced features such as AI-powered recommendations, augmented reality experiences for menu visualization, and seamless online ordering and reservation systems. Consumers are increasingly relying on these apps for discovering new restaurants, comparing menus and prices, reading reviews, and making reservations, all within a single platform. The market segmentation reveals a strong demand across various dining styles, from quick bites and street food to business dining, indicating broad appeal across demographic groups. While platform variations (Android and iOS) contribute to the market size, the majority of users utilize mobile devices for accessing these services. Leading players like Yelp, Zomato, and TripAdvisor are actively competing by enhancing user experience through improved search algorithms, personalized recommendations, and loyalty programs. However, challenges remain, including the need to maintain data accuracy, manage user reviews effectively, and compete with the continuous emergence of new entrants. The market is geographically diverse, with North America and Asia Pacific exhibiting significant growth potential. The forecast period (2025-2033) suggests continued growth, driven by technological advancements and changing consumer behavior. A strong CAGR (let's assume a conservative 15% based on similar tech markets) would indicate substantial market expansion. The competitive landscape remains dynamic, with established players consolidating their market share through strategic acquisitions and partnerships. Furthermore, the integration of social media features within restaurant guide apps is expected to amplify user engagement and drive further growth. The market will likely witness increased focus on personalized experiences, enhanced data security, and the adoption of innovative marketing strategies. Regions with high smartphone penetration and a growing middle class are likely to witness the most significant growth.

Time spent maintaining the social media campaign on a weekly basis.

Introduction

Facebook vs Instagram vs Twitter Statistics: We all spend time on social media every day, but have you ever thought about which app works best for you? Whether you're running a business, creating content, or want to keep in touch and stay updated, picking the right platform makes a big difference. Facebook, Instagram, and Twitter (now known as X) all have distinct features, user types, and benefits.

Knowing how each one works can help you save time and even make more money. In this guide, "Facebook vs Instagram vs Twitter - Which is Better?†, we’ll look at what each platform does well, who uses them, and how they can help you reach your goals so you can decide which one fits your needs best.

Social media is increasingly used to engage persons with lived experience and healthcare professionals in research, however, there remains sparse guidance on how to effectively use social media to engage these groups in research agenda-setting. Here we report our process and experience utilizing a social media campaign to engage Canadians within the pediatric cancer community in a research priority-setting exercise. Following the James Lind Alliance method, we launched a priority-setting partnership (PSP) to develop a child with cancer-, survivor-, family member-, and healthcare professional-based Canadian pediatric cancer research agenda. Social media-based strategies were implemented to recruit participants for two PSP surveys, including preparatory activities, developing a website, launching graphics and advertisements, and engaging internal and external networks. Descriptive statistics of our data and analytics provided by the platforms are used presently to report our process. The framework we implemented involved preparing for social media use, identifying a target audience, developing campaign content, conducting the campaign, refining the campaign as needed, and evaluating its success. Our process resulted in a substantial social media-based reach, good survey completion rates, and a successfully developed pediatric cancer community-specified research agenda. Social media may represent a useful approach to engage persons with lived experience and healthcare professionals in research agenda development. Based on our experience, we present strategies to increase social media campaign engagement that may be useful to those seeking to conduct health research priority-setting exercises.

Companion DATA

Title:

Using social media and personality traits to assess software developers’ emotions

Authors:

Leo Moreira Silva

Marília Gurgel Castro

Miriam Bernardino Silva

Milena Nestor Santos

Uirá Kulesza

Margarida Lima

Henrique Madeira

Journal:

PeerJ Computer Science

------------------------------------------------------------

The folders contain:

/analysis

analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists

analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants

analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications

/dataset

alldata.json: contains the dataset used in the paper

/ethics_committee

committee_response.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra.

committee_submission_form.pdf: the project submitted to the committee.

consent_form.pdf: declaration of free and informed consent fulfilled by participants.

data_protection_declaration.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation.

/notebooks

General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper

Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study

Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results

Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results

Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis

Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period

/surveys

Demographic_Survey.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts

Demographic_Survey_answers.xlsx: participants' demographic survey answers

ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits

ibf_answers.xlsx: participantes' and psychologists' answers for BFI

Experiment Protocol.pdf: file containing the explanation of the experiment protocol.

------------------------------------------------------------

We have removed from dataset any sensible data to protect participants' privacy and anonymity.

We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.

In order to develop appropriate tools (e.g. a mobile app) we explored through a participant survey the issues such as the kinds of media coverage that engage and inform voters, whether and how this varies by subgroups such as generation, and the aspects of campaigns that contribute to more positive views of the political process. As part of ExpoNet's objectives to understand news and information exposure in the contemporary environment, we worked to to enhancing the quality of representative democracy through giving better access to citizens to quality information and the tools necessary to evaluate the news they consumed. By providing information about the nature and quality of traditional and new media election coverage over time and its impact on individuals, our research will offer pointers towards how to mobilize informed engagement with campaigns and in elections. The advent of Web 2.0 - the second generation of the World Wide Web, that allows users to interact, collaborate, create and share information online, in virtual communities - has radically changed the media environment, the types of content the public is exposed to as well as the exposure process itself. Individuals are faced with a wider range of options (from social and traditional media), new patterns of exposure (socially mediated and selective), and alternate modes of content production (e.g. user-generated content). In order to understand change (and stability) in opinions and behaviour, it is necessary to measure to what information a person has been exposed. The measures social scientists have traditionally used to capture information exposure usually rely on self-reports of newspaper reading and television news broadcast viewing. These measures do not take into account that individuals browse and share diverse information from social and traditional media on a wide range of platforms. According to the OECD's Global Science Forum 2013 report, social scientists' inability to anticipate the Arab Spring was partly due to a failure to understand 'the new ways in which humans communicate' via social media and the ways they are exposed to information. And social media's mixed record for predicting the results of recent UK elections suggests better tools and a unified methodology are needed to analyze and extract political meaning from this new type of data. We argue that a new set of tools, which models exposure as a network and incorporates both social and traditional media sources, is needed in the social sciences to understand media exposure and its effects in the age of digital information. Whether one is consuming the news online or producing/consuming information on social media, the fundamental dynamic of consuming public affairs news involves formation of ties between users and media content by a variety of means (e.g. browsing, social sharing, search). Online media exposure is then a process of network formation that links sources and consumers of content via their interactions, requiring a network perspective for its proper understanding. We propose a set of scalable network-oriented tools to 1) extract, analyse, and measure media content in the age of "big media data", 2) model the linkages between consumers and producers of media content in complex information networks, and 3) understand co-development of network structures with consumer attitudes/behaviours. In order to develop and validate these tools, we bring together an interdisciplinary and international team of researchers at the interface of social science and computer science. Expertise in network analysis, text mining, statistical methods and media analysis will be combined to test innovative methodologies in three case studies including information dynamics in the 2015 British election and opinion formation on climate change. Developing a set of sophisticated network and text analysis tools is not enough, however. We also seek to build national capacity in computational methods for the analysis of online 'big' data. The survey responses were collected from an online panel run by Dynata. There are 1802 respondents across a range of responses to attitudes and practices of using social media. Demographic variables have also been included. Like other companies where online samples can be purchased, Dynata uses invitations of all types including e-mail invitations, phone alerts, banners and messaging on panel community sites to include people with a diversity of motivations to take part in research. Respondents are paid for completing surveys. In terms of quality control, Dynata checks for duplicate participants by evaluating variables such as email address, matches across several demographic data, and device-related data through use of digital fingerprint technology. Participants are then directed to our survey, programmed in Qualtrics, that is hosted on a server at the University of Exeter in order to comply with data protection and privacy guidelines.