The number of Facebook users in the United States was forecast to continuously increase between 2024 and 2028 by in total 12.6 million users (+5.04 percent). After the ninth consecutive increasing year, the Facebook user base is estimated to reach 262.8 million users and therefore a new peak in 2028. Notably, the number of Facebook users of was continuously increasing over the past years.User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

As of April 2024, Facebook had an addressable ad audience reach 131.1 percent in Libya, followed by the United Arab Emirates with 120.5 percent and Mongolia with 116 percent. Additionally, the Philippines and Qatar had addressable ad audiences of 114.5 percent and 111.7 percent.

This statistical dataset contains estimates on the number of active online Facebook users living outside of their country of origin within the European Union. The dataset includes information on Facebook users' age, gender, country of residence, and country of previous residence. The data is divided in the number of Monthly Active Users and Daily Active Users. The data was collected through standard CSV format via an advertising API platform by using an R Studio code, and the data collection was conducted twice a month from January to November 2021.

The dataset was originally published in DiVA and moved to SND in 2024.

During the fourth quarter of 2023, the number of daily active users on Facebook reached 2.1 billion, a minor increase on the previous quarter. When compared with the number of daily active users in the final quarter of 2022, the platform has gained around 100 million users. Facebook’s penetration rate for the United States in 2023 was 72.13 percent, up from 71.43 percent in 2022. The social network’s audience reach is projected to stand at 75.79 percent by 2027.

Most popular social media websites

As of May 2021, Facebook was the most used social media site in the United States, accounting for 71.8 percent of all social media visits. Ranking in second place was Pinterest with 12.4 percent, followed by Twitter and Instagram, with 9.15 percent and 3.82 percent, respectively. Although other sites remain popular, Facebook’s number of visits made it undoubtably the leading social media platform in terms of social media site visits.

For Generation Z and Millennials in the United States, Facebook was one of the least popular platforms used to connect with others. Gen Z and Millennials preferred video sharing platforms, specifically Snapchat, TikTok and YouTube.

Meta’s revenue

Facebook Inc was renamed as Meta in 2021, in a strategic step toward the metaverse. Meta Platforms is now the parent company of Facebook, Instagram, Facebook Messenger and WhatsApp amongst others, together being known as Meta’s Family of Apps.

Meta’s annual revenue for 2021 was 117.92 billion U.S. dollars, up from 85.97 billion in 2020. Within a decade, the company has increased its annual revenue by approximately 114 billion U.S. dollars. In the most recent fiscal year, Meta’s Family of Apps were responsible for over 115 billion U.S. dollars’ worth of Meta’s revenue.

1) Data Introduction • The Social Media Usage Dataset(Applications) features patterns and activity indicators that 1,000 users use seven major social media platforms, including Facebook, Instagram, and Twitter.

2) Data Utilization (1) Social Media Usage Dataset(Applications) has characteristics that: • This dataset provides different social media activity data for each user, including daily usage time, number of posts, number of likes received, and number of new followers. (2) Social Media Usage Dataset(Applications) can be used to: • Analysis of User Participation by Platform: You can analyze participation and popular trends by platform by comparing usage time and activity for each social media. • Establish marketing strategy: Based on user activity data, it can be used for targeted marketing, content production, and user retention strategies.

As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

              Facebook connects the world

              Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
              as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

Context

The Dataset is uploaded in ZIP format. The dataset contains 5 variants of the dataset, for the details about the variants and detailed analysis read and cite the research paper TITLE='Comment Volume Prediction

Content

28 columns content in this Dataset 1] Describing popularity or support for the source. 2] Describe how many prople so far visited this place 3]Defines the daily interest of individuals towards source of the document/ Post. 4]Defines the daily interest of individuals towards source of the document/ Post.

Context: This dataset offers insights into the usage patterns of social media apps for 1,000 users across seven popular platforms: Facebook, Instagram, Twitter, Snapchat, TikTok, LinkedIn, and Pinterest. It tracks various metrics such as daily time spent on the app, number of posts made, likes received, and new followers gained.

Dataset Features:

User_ID: Unique identifier for each user. App: The social media platform being used. Daily_Minutes_Spent: Total time a user spends on the app each day, ranging from 5 to 500 minutes. Posts_Per_Day: Number of posts a user creates per day, ranging from 0 to 20. Likes_Per_Day: Total number of likes a user receives on their posts each day, ranging from 0 to 200. Follows_Per_Day: The number of new followers a user gains daily, ranging from 0 to 50. Context & Use Cases: This dataset could be particularly useful for social media analysts, digital marketers, or researchers interested in understanding user engagement trends across different platforms. It provides insights into how much time users spend, how actively they post, and the level of engagement they receive (in terms of likes and followers).

Conclusion & Outcome: Analyzing this dataset could yield several outcomes:

Engagement Patterns: Identifying which platforms have higher engagement in terms of time spent or likes received. Active Users: Determining which users are the most active across various platforms based on the number of posts and followers gained. User Retention: Studying the correlation between time spent and follower growth, providing insight into user retention strategies for different platforms. Overall, the dataset allows for exploration of social media usage trends and helps drive decision-making for marketing strategies, content creation, and platform engagement.

Analysis of ‘prediction of facebook comment’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kiranraje/prediction-facebook-comment on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Dataset is uploaded in ZIP format. The dataset contains 5 variants of the dataset, for the details about the variants and detailed analysis read and cite the research paper TITLE='Comment Volume Prediction

Content

28 columns content in this Dataset 1] Describing popularity or support for the source. 2] Describe how many prople so far visited this place 3]Defines the daily interest of individuals towards source of the document/ Post. 4]Defines the daily interest of individuals towards source of the document/ Post.

--- Original source retains full ownership of the source dataset ---

This dataset is a collection of 12,478 social media comments found on the official Facebook pages of ten Philippine newspapers, The Philippine Daily Inquirer, Manila Bulletin, The Philippine Star, The Manila Times, Sunstar Cebu, Sunstar Davao, Cebu Daily News, The Freeman, Sunstar Davao, MindaNews, and The Mindanao Times, spanning the years 2015, 2017 and 2019. The comments contain terms related to the Moro identity and the Mamasapano Clash, the Marawi Siege and the establishment of BARMM in the southern Philippines, allowing researchers to study semantic fields with regard to Muslims and the relationship between the texts and the source newspaper, their region of origin, and political administration, among other variables. All comments in the dataset were downloaded through Facebook's Graph API via Facepager (Jünger & Keyling, 2019).

One CSV file (MMB151719SOCMED_v2.csv) is provided, along with a codebook that contains descriptions of the variables and codes used in the CSV file, and a Readme document with a changelog.

Each social media comment is annotated with the following metadata:

object_id: identifier associated with the comment;

message: the textual string of the comment;

message_proc: the textual string of the comment after pre-processing;

lang_label: categorical value for the language of the comment (Tagalog (Filipino), Cebuano, English, Taglish, Bislog, Bislish, Trilingual or Other);

from_name: identifier of public pages (not profiles of individuals) leaving comments (NaN for profiles of individuals, 'NAME' for public pages besides the newspapers, otherwise, the page name of the newspaper);

created_time: Facebook Graph API's-generated string for the date and time the comment was posted;

month_year: categorical value in the form string+YY (e.g. Jun-15) of the month and year when the comment was posted;

year: numerical value in the form YY;

newspaper: categorical value for the newspaper Facebook page under which the comment was found;

corpus: categorical value for comments from the main corpus or the side (control) corpus;

administration: categorical value for political administration (pbsa = President Benigno Aquino III, prrd = President Rodrigo Roa Duterte);

count: numerical value referring to the number of string sequences without spaces;

The dataset may only be used for non-commercial purposes and is licensed under the CC BY-NC-SA 4.0 DEED.

V2 - 05/06/2024

Corrections

Corrections made to region to include Luzon, Visayas and Mindanao (as opposed to Mindanao, non-Mindanao);

Corrections made to administration coding.

This dataset is described by:

Cruz, F. A. (2024). A Multilingual Collection of Facebook Comments on the Moro Identity and Armed Conflict in the Southern Philippines. Journal of Open Humanities Data, 10(1), 41. DOI: https://doi.org/10.5334/johd.219

Bibiliography

Jünger, J., & Keyling, T. (2019). Facepager: An application for automated data retrieval on the web (4.5.3) [Computer software]. https://github.com/strohne/Facepager/

This dataset measures the ideological segregation index and favorability score of the potential, exposed and engaged audience of posts with links to domains and URLs classified as civic news. The dataset contains domain- and URL-level metrics from Facebook activity data for adult U.S. monthly active users, aggregated daily over the study period. Includes ideological segregation index, favorability score, content attributes, user attributes.

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.

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/"

Context

This dataset contains daily stock data for Meta Platforms, Inc. (META), formerly Facebook Inc., from May 19, 2012, to January 20, 2025. It offers a comprehensive view of Meta’s stock performance and market fluctuations during a period of significant growth, acquisitions, and technological advancements. This dataset is valuable for financial analysis, market prediction, machine learning projects, and evaluating the impact of Meta’s business decisions on its stock price.

Content

The dataset includes the following key features:

Open: Stock price at the start of the trading day. High: Highest stock price during the trading day. Low: Lowest stock price during the trading day. Close: Stock price at the end of the trading day. Adj Close: Adjusted closing price, accounting for corporate actions like stock splits, dividends, and other financial adjustments. Volume: Total number of shares traded during the trading day.

Variables

Date: The date of the trading day, formatted as YYYY-MM-DD. Open: The stock price at the start of the trading day. High: The highest price reached by the stock during the trading day. Low: The lowest price reached by the stock during the trading day. Close: The stock price at the end of the trading day. Adj Close: The adjusted closing price, which reflects corporate actions like stock splits and dividend payouts. Volume: The total number of shares traded on that specific day.

Acknowledgements

This dataset was sourced from reliable public APIs such as Yahoo Finance or Alpha Vantage. It is provided for educational and research purposes and is not affiliated with Meta Platforms, Inc. Users are encouraged to adhere to the terms of use of the original data provider.

Facebook is becoming an essential tool for more than just family and friends. Discover how Cheltenham Township (USA), a diverse community just outside of Philadelphia, deals with major issues such as the Bill Cosby trial, everyday traffic issues, sewer I/I problems and lost cats and dogs. And yes, theft.

Communities work when they're connected and exchanging information. What and who are the essential forces making a positive impact, and when and how do conversational threads get directed or misdirected?

Use Any Facebook Public Group

You can leverage the examples here for any public Facebook group. For an example of the source code used to collect this data, and a quick start docker image, take a look at the following project: facebook-group-scrape.

Data Sources

There are 4 csv files in the dataset, with data from the following 5 public Facebook groups:

• Unofficial Cheltenham Township
• Elkins Park Happenings!
• Free Speech Zone
• Cheltenham Lateral Solutions
• Cheltenham Township Residents

post.csv

These are the main posts you will see on the page. It might help to take a quick look at the page. Commas in the msg field have been replaced with {COMMA}, and apostrophes have been replaced with {APOST}.

• gid Group id (5 different Facebook groups)
• pid Main Post id
• id Id of the user posting
• name User's name
• timeStamp
• shares
• url
• msg Text of the message posted.
• likes Number of likes

comment.csv

These are comments to the main post. Note, Facebook postings have comments, and comments on comments.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Comment Id.
• timeStamp
• id Id of user commenting
• name Name of user commenting
• rid Id of user responding to first comment
• msg Message

like.csv

These are likes and responses. The two keys in this file (pid,cid) will join to post and comment respectively.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Matches Comments id.
• response Response such as LIKE, ANGRY etc.
• id The id of user responding
• name Name of the user responding

member.csv

These are all the members in the group. Some members never, or rarely, post or comment. You may find multiple entries in this table for the same person. The name of the individual never changes, but they change their profile picture. Each profile picture change is captured in this table. Facebook gives users a new id in this table when they change their profile picture.

• gid Group id
• id Id of the member
• name Name of the member
• url URL of the member

56.8% of the world’s total population is active on social media.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Facebook settlement claim process

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Context

Time series modelling for the prediction of stocks prices is a challenging task. Political events, market expectations and economic factors are just a few known factors that can impact financial market behaviour. The financial market is a complex, noisy, evolutionary and chaotic field of study that attracts many enthusiasts and researches — the first, usually driven by the economic benefit of it, the latter, inspired by the challenge of handling such complex data.

This project aims to predict Facebook (FB) next day stock price direction with machine learning algorithms. Technical indicators and global market indexes are used, and their influence on the forecast accuracy is analysed.

Content

Daily values were retrieved (volume, open, close, low and high prices) from Yahoo! Finance website. For Facebook (FB), July 2012 was the earliest data available. The date range is July 2012 to November 2018.

The closing price of current day C(t) and closing price from the previous day C(t-1) are compared to build the initial dataset. The objective is to define if the price trend is going up or down by analysing these two values. For each instance, a comparison was made and recorded. If the price is going up, C(t) > C(t-1), class “1” is assigned. Class “0” is assigned for the opposite case.

• ID: Sample ID
• Close: Closing value of previous day
• Low: Lowest value of previous day
• High: Highest value of previous day
• Volume: Volume value of previous day

Research was initiated to understand which features could help the model to forecast the stock direction. Three main routes were found: Lag features, Technical Indicators and Global Market Indexes. Below is an explanation of each group of features.

Lag features are features that contain the closing price and direction of previous days and it is a common strategy for Time Series models. The following features were added:

• C(t-5): Closing price of 5 days before
• C(t-4): Closing price of 4 days before
• C(t-3): Closing price of 3 days before
• C(t-2): Closing price of 2 days before
• C_up_4: Output 1 if closing price went up 4 days ago
• C_up_3: Output 1 if closing price went up 3 days ago
• C_up_2: Output 1 if closing price went up 2 days ago
• C_up_1: Output 1 if closing price went up 1 day ago

Technical indicators are used by researches and financial market analysts to support stock market trend forecasting. Common indicators retrieved from the literature were selected and calculated for Facebook stock. Techical Indicators added:

• MA-10: Moving Average considering previous 10 days
• MA-5: Moving Average considering previous 5 days
• WMA-10: Weighted Moving Average considering previous 10 days
• SO: Stochastic Oscillator
• M: Momentum as the difference in closing price in a 10 days interval
• SSO: Slow Stochastic Oscillator
• EMA: Exponential Moving Average for a 10 day period
• MACD_Sline_9: MACD Signal Line for a 9 day period
• RSI: Relative Strength Index
• CCI: Commodity Channel Index
• ADO: Accumulation Distribution Oscillator

Technical indicators provide a suggestion of the stock price movement. Additional features were created for each technical indicator by analysing its daily value and assigning a class according to their meaning. Class “1” is given if the indicator numerical value suggests upper trend, class “0” for a downtrend. In other words, financial market analysis is performed at a simplistic level, in the attempt to translate what the continuous value means.

• MA-10>C: If MA-10 is higher than Closing price output 1
• MA-5>C: If MA-5 is higher than Closing price output 1
• WMA-10>C: If WMA-10 is higher than Closing price output 1
• SO>SOt-1: Output is 1 if SO current value is higher than previous day
• M>0: A positive momentum outputs 1
• SSO>SSOt-1: SSO current value is higher than previous day
• EMA>C: If EMA is higher than Closing price output 1
• MACD>MACDt-1: If MACD current value is higher than previous day output 1
• RSI70-30: If RSI is above 70, output 0. Values below 30 output is one. For values within this range it compares to previous day and outputs 1 if value has increased
• CCI200-200: Similar to RSI, but if threshold set for 200 and -200.
• ADO>ADOt-1: Output is 1 if ADO current value is higher than previous day

For a given country or region, the stock market index characterises the performance of its financial market and the overall local economy. For this reason, the same day performance of these markets could contribute to the machine learning model predictions. Six global indexes were added as features, with their closing direction as up or down, class “1” or “0”, respectively. Data for these indexes (Nikkei, Hang Seng, All Ordinaries, Euronext 100, SSE and DAX) were also retrieved from Yahoo! Finance.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.