The number of social media users in the United States was forecast to continuously increase between 2024 and 2029 by in total 26 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 330.07 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.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).

Problem Statement

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A global consumer goods company struggled to understand customer sentiment across various social media platforms. With millions of posts, reviews, and comments generated daily, manually tracking and analyzing public opinion was inefficient. The company needed an automated solution to monitor brand perception, address negative feedback promptly, and leverage insights for marketing strategies.

Challenge

Analyzing social media sentiment posed the following challenges:

Processing vast amounts of unstructured text data from multiple platforms like Twitter, Facebook, and Instagram.

Accurately interpreting slang, emojis, and nuanced language used by social media users.

Identifying trends and actionable insights in real-time to respond to potential crises or opportunities effectively.

Solution Provided

An advanced sentiment analysis system was developed using Natural Language Processing (NLP) and sentiment analysis algorithms. The solution was designed to:

Classify social media posts into positive, negative, and neutral sentiments.

Extract key topics and trends related to the brand and its products.

Provide real-time dashboards for monitoring customer sentiment and identifying areas of improvement.

Development Steps

Data Collection

Aggregated data from major social media platforms using APIs, focusing on brand mentions, hashtags, and product keywords.

Preprocessing

Cleaned and normalized text data, including handling slang, emojis, and misspellings, to prepare it for analysis.

Model Training

Trained NLP models for sentiment classification using supervised learning. Implemented topic modeling algorithms to identify recurring themes and discussions.

Validation

Tested the sentiment analysis models on labeled datasets to ensure high accuracy and relevance in classifying social media posts.

Deployment

Integrated the sentiment analysis system with a real-time analytics dashboard, enabling the marketing and customer support teams to track trends and respond proactively.

Monitoring & Improvement

Established a continuous feedback mechanism to refine models based on evolving language patterns and new social media trends.

Results

Gained Actionable Insights

The system provided detailed insights into customer opinions, helping the company identify strengths and areas for improvement.

Improved Brand Reputation Management

Real-time monitoring enabled swift responses to negative feedback, mitigating potential reputation risks.

Informed Marketing Strategies

Insights from sentiment analysis guided targeted marketing campaigns, resulting in higher engagement and ROI.

Enhanced Customer Relationships

Proactive engagement with customers based on sentiment analysis improved customer satisfaction and loyalty.

Scalable Monitoring Solution

The system scaled efficiently to analyze data across multiple languages and platforms, broadening the company’s reach and understanding.

How much time do people spend on social media? As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 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 three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just two 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.

Survey instrument and anonymised responses collected as part of Sub-Project B4 “Provenance of Social Media” of the larger Social Media - Developing Understanding, Infrastructure & Engagement (Social Media Enhancement) award (ES/M001628/1). The survey aimed to further our understanding of the current practices and attitudes towards the provenance of data collected from social media platforms and its analysis by researchers in the social sciences. This includes all forms of social media, such as Twitter, Facebook, Wikipedia, Quora, blogs, discussion forums, etc. The survey was conducted as an online-survey using Google Forms. Findings from this survey influenced the work of the sub-project, and the development of tools to support researchers who wish to increase the transparency of their research using social media data.

Dataset of collected survey responses, and pdf versions of the Google Forms online survey instrument. Each PDF file denotes one possible survey path that depended on the response of a participant to the question “What level of experience do you have using data from a social media platforms as part of your research?” The three paths are:

(1) SurveyInstrument-Path-1.pdf - is used if the participant selected the option "I have used/am currently using social media data as part of my research."

(2) SurveyInstrument-Path-2.pdf - is used if the participant selected the option "I am aware of others using social media data as part of their research and may consider using it within mine."

(3) SurveyInstrument-Path-3.pdf - is used if the participant selected the option "Neither of the above."

There is now a broad consensus that new forms of social data emerging from people’s day-to-day activities on the web have the potential to transform the social sciences. However, there is also agreement that current analytical techniques fall short of the methodological standards required for academic research and policymaking and that conclusions drawn from social media data have much greater utility when combined with results drawn from other datasets (including various public sector resources made available through open data initiatives). In this proposal we outline the case for further investigations into the challenges surrounding social media data and the social sciences. Aspects of the work will involve analysis of social media data in a number of contexts, including: -transport disruption around the 2014 Commonwealth Games (Glasgow) - news stories about Scottish independence and UK-EU relations - island communities in the Western Isles. Guided by insights from these case studies we will: - develop a suite of software tools to support various aspects of data analysis and curation; - provide guidance on ethical considerations surrounding analysis of social media data; - deliver training workshops for social science researchers; - engage with the public on this important topic through a series of festivals (food, music, science).

More than 100 social media channels and statistics for the National Archives and Records Administration.

Dataset

This dataset was created by Srijan Sharma

Contents

The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, 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.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).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

Researcher(s): Alexandros Mokas, Eleni Kamateri

Supervisor: Ioannis Tsampoulatidis

This repository contains 3 social media datasets:

2 Post-processing datasets: These datasets contain post-processing data extracted from the analysis of social media posts collected for two different use cases during the first two years of the Deepcube project. More specifically, these include:

• The UC2 dataset containing the post-processing analysis of the Twitter data collected for the DeepCube use case (UC2) dealing with the climate induced migration in Africa. This dataset contains in total 5,695,253 social media posts collected from the Twitter platform, based on the initial version of search criteria relevant to UC2 defined by Universitat De Valencia, focused on the regions of Ethiopia and Somalia and started from 26 June, 2021 till March, 2023.
• The UC5 dataset containing the post-processing analysis of the Twitter and Instagram data collected for the DeepCube use case (UC5) related to the sustainable and environmentally-friendly tourism. This dataset contains in total 58,143 social media posts collected from the Twitter and Instagram platform (12,881 collected from Twitter and 45,262 collected from Instagram), based on the initial version of search criteria relevant to UC5 defined by MURMURATION SAS, focused on the regions of Brasil and started from 26 June, 2021 till March, 2023.

1 Annotated dataset: An additional anottated dataset was created that contains post-processing data along with annotations of Twitter posts collected for UC2 for the years 2010-2022. More specifically, it includes:

• The UC2 dataset contain the post-processing of the Twitter data collected for the DeepCube use case (UC2) dealing with the climate induced migration in Africa. This dataset contains in total 1721 annotated (412 relevant and 1309 irrelevant) by social media posts collected from the Twitter platform, focused on the region of Somalia and started from 1 January, 2010 till 31 December, 2022.

For every social media post retrieved from Twitter and Instagram, a preprocessing step was performed. This involved a three-step analysis of each post using the appropriate web service. First, the location of the post was automatically extracted from the text using a location extraction service. Second, the images included in the post were analyzed using a concept extraction service, which identified and provided the top ten concepts that best described the image. These concepts included items such as "person," "building," "drought," "sun," and so on. Finally, the sentiment expressed in the post's text was determined by using a sentiment analysis service. The sentiment was classified as either positive, negative, or neutral.

After the social media posts were preprocessed, they were visualized using the Social Media Web Application. This intuitive, user-friendly online application was designed for both expert and non-expert users and offers a web-based user interface for filtering and visualizing the collected social media data. The application provides various filtering options, an interactive map, a timeline, and a collection of graphs to help users analyze the data. Moreover, this application provides users with the option to download aggregated data for specific periods by applying filters and clicking the "Download Posts" button. This feature allows users to easily extract and analyze social media data outside of the web application, providing greater flexibility and control over data analysis.

The dataset is provided by INFALIA.

INFALIA, being a spin-off of the CERTH institute and a partner of a research EU project, releases this dataset containing Tweets IDs and post pre-processing data for the sole purpose of enabling the validation of the research conducted within the DeepCube. Moreover, Twitter Content provided in this dataset to third parties remains subject to the Twitter Policy, and those third parties must agree to the Twitter Terms of Service, Privacy Policy, Developer Agreement, and Developer Policy (https://developer.twitter.com/en/developer-terms) before receiving this download.

The dataset contains 31 transcribed and anonymized interviews of blockchain-based social media users. The dataset was collected during the summer of 2022 as part of a research project at the Social Media Lab at Toronto Metropolitan University. The dataset is available upon request for validation by peer-reviewers or other researchers in the field.

Facebook and YouTube are still the most used social media platforms today.

ABSTRACT

The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.

INTRODUCTION

Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.

The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).

Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).

These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.

PARTICIPANTS

The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).

DATE STRUCTURE AND ARCHIVES FORMAT

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

Social network

The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.

Personal networks

Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).

Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.

Sense of community and metropolitan displacements

The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:

 • Socio-economic data.


 • Data on habitual residence.


 • Information on intercity journeys.


 • Identity and sense of community.


 • Personal network indicators.


 • Social network indicators.

DATA ACCESS

Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.

The visual representation of the personal networks of the respondents in both waves is available in the following album of the Graphic Gallery of Personal Networks on Flickr: https://www.flickr.com/photos/25906481@N07/albums/72157667029974755.

In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:

Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp

The English version of this article can be downloaded from: https://tinyurl.com/yy9s2byl

CONCLUSION

The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.

The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.

ACKNOWLEDGEMENTS

The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals, groups, organizations and social settings” (2006 -2009) of the European Science Foundation (ESF). The data was presented for the first time on June 30, 2009, at the European Research Collaborative Project Meeting on Dynamic Analysis of Networks and Behaviors, held at the Nuffield College of the University of Oxford.

REFERENCES

Brandes, U., & Wagner, D. (2004). Visone - Analysis and Visualization of Social Networks. In M. Jünger, & P. Mutzel (Eds.), Graph Drawing Software (pp. 321-340). New York: Springer-Verlag.

Maya-Jariego, I. (2018). Why name generators with a fixed number of alters may be

List of social media sites by agency

Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorized database of accounts of influence on Twitter in India, identified and annotated through an iterative process of friends, networks, and self-described profile information, verified manually. We built an initial set of accounts based on the friend network of a seed set of accounts based on real-world renown in various fields, and then snowballed friends of friends\" multiple times, and rank ordered individuals based on the number of in-group connections, and overall followers. We then manually classified identified accounts under the categories of entertainment, sports, business, government, institutions, journalism, civil society accounts that have independent standing outside of social media, as well as a category ofdigital first" referring to accounts that derive their primary influence from online activity. Overall, we annotated 11580 unique accounts across all categories. The database is useful studying various questions related to the role of influencers in polarisation, misinformation, extreme speech, political discourse etc.

Dataset

This dataset was created by soroush khandouzi

Contents

This dataset contains a range of directed signed networks (signed digraphs) from social domain. The data come from 9 different sources and in total there are 29 network files. There are two temporal networks and one multilayer network in this dataset. Each network is provided in two formats: edgelist (.csv) and .gml format.This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as they provide citations to this data repository (https://doi.org/10.6084/m9.figshare.12152628) and the reference article listed below (https://doi.org/10.1038/s41598-020-71838-6), and license the new creations under the identical terms.For more information about the data, one may refer to the article below:Samin Aref, Ly Dinh, Rezvaneh Rezapour, and Jana Diesner. "Multilevel Structural Evaluation of Signed Directed Social Networks based on Balance Theory" Scientific Reports (2020) https://doi.org/10.1038/s41598-020-71838-6

Dataset

This dataset was created by OMR ABDULLAH

Released under Other (specified in description)

Contents

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, 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. Reddit users encompass both users that are logged in and those that are not.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).Find more key insights for the number of Reddit users in countries like Mexico and Canada.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

• ID: A unique identifier for each tweet.
• text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.
• polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).
• favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.
• retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.
• user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.
• user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.
• user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.
• user_followers_count: The current number of followers the account has. It is a non-negative integer.
• user_friends_count: The number of users that the account is following. It is a non-negative integer.
• user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.
• user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.
• user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.
• user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

• Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.
• Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.
• Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

Promoted Social Media Posts marketing different programs and departments in Pierce County government

This dataset supports research on how engagement with social media (Instagram and TikTok) was related to problematic social media use (PSMU) and mental well-being. There are three different files. The SPSS and Excel spreadsheet files include the same dataset but in a different format. The SPSS output presents the data analysis in regard to the difference between Instagram and TikTok users.