The social media penetration rate in Singapore was forecast to continuously increase between 2024 and 2028 by in total 4.6 (+4.86 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 99.24 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

The number of social media users in Singapore was forecast to continuously increase between 2024 and 2029 by in total 0.2 million users (+3.6 percent). After the eleventh consecutive increasing year, the social media user base is estimated to reach 5.75 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).

As of January 2023, 89.5% of the total population in Singapore uses social media. That is 5.30 million Singaporean social media users.

As of May 2024, ** percent of Gen Z respondents spent more time on social media than they did a year ago. Millennials showed a similar dynamic, with ** percent stating that their social media use had increased compared to the previous year.

It comes as no surprise that the largest social network in Singapore is the messenger app WhatsApp and the social media giant Facebook. With Singapore home to one of the fastest internet connection speed in the world, the convenience of using these social networks on a daily basis has driven the growth of online content. There are over *** million social media users in Singapore As of 2024, Singapore’s population has reached over *********** people. These are, however, on an island with an area of only ***** square kilometers. The high technological standard has led to the internet penetration being extraordinarily high compared to other Southeast Asian countries. A high Internet penetration rate is usually a first indication that social media has great importance for the country too. In this case the general number of social media users amounted to **** million people, reaching **** percent of the population. While WhatsApp is still the most popular social media channel in Singapore, it seems like Facebook is currently losing their younger users. Young Singaporean social media users would instead be flocking to Instagram, Snapchat or TikTok.

According to a survey on social commerce conducted by Rakuten Insight in Singapore in 2025, approximately ** percent of respondents between 16 and 24 years answered that they used TikTok for purchases during the last 12 months. The same survey indicated that around ** percent of Singaporean consumers regularly shopped on social media.

Singapore Internet Usage: Social Media Market Share: All Platforms: Hi5 data was reported at 0.000 % in 16 Mar 2025. This stayed constant from the previous number of 0.000 % for 15 Mar 2025. Singapore Internet Usage: Social Media Market Share: All Platforms: Hi5 data is updated daily, averaging 0.000 % from Mar 2025 (Median) to 16 Mar 2025, with 9 observations. The data reached an all-time high of 0.070 % in 12 Mar 2025 and a record low of 0.000 % in 16 Mar 2025. Singapore Internet Usage: Social Media Market Share: All Platforms: Hi5 data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Singapore – Table SG.SC.IU: Internet Usage: Social Media Market Share.

This study delves into analyzing social media data sourced from Twitter within the context of Singapore, forming a crucial component of a broader social listening initiative. We provide a decade’s worth of social data from Singapore, offering invaluable insights for the research community. This work presents two analytical approaches utilizing this dataset: sentiment analysis and bursty topic detection. Sentiment analysis for direct search is based on zero shot pretrained model while busrty topic analysis is based on biterm topic model. The detailed experiments demonstrate the efficacy of the approach for analyzing social trends using Twitter data. We collected all twitter data posted in Singapore from 2008 to 2023. The geocode setting as (1.346353, 103.807526, 25km) was used in Twitter API to cover the whole of Singapore. The total number of tweets in this dataset is 96,686,894. There are 3 data files: 1. place.json includes 10k detailed places information in Singapore.2.subzones.json includes 332 subzone information in Singapore 3.tweets.json includes 96M+tweets posted in Singapore. MongoDB was used as the database to store and manage the data.

The number of Instagram users in Singapore was forecast to continuously decrease between 2024 and 2028 by in total 0.5 million users (-15.82 percent). According to this forecast, in 2028, the Instagram user base will have decreased for the eighth consecutive year to 2.64 million users. User figures, shown here with regards to the platform instagram, 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).Find more key insights for the number of Instagram users in countries like Vietnam and Malaysia.

The purpose of study is to investigate how individual features, such as personality traits, biological sex, age, influence the quantity and type of usage of social media platforms across the spectrum of psychological well-being during the time of circuit breaker due to the Covid-19 pandemic. Results found that personality traits were differently predictive for active and passive usage of Facebook, Instagram, and Twitter during the pandemic. Moreover, results highlighted that psychosocial aspects of social media usage were correlated to anxiety and depressive symptoms experienced during the circuit breaker. The research protocol has been approved by NTU Institutional Board (NTU-IRB). The NTU-IRB reference number for this study is IRB-2020-04-041.

Singapore Internet Usage: Social Media Market Share: All Platforms: Youku data was reported at 0.000 % in 06 Jan 2025. This stayed constant from the previous number of 0.000 % for 05 Jan 2025. Singapore Internet Usage: Social Media Market Share: All Platforms: Youku data is updated daily, averaging 0.000 % from Jun 2023 (Median) to 06 Jan 2025, with 274 observations. The data reached an all-time high of 2.200 % in 19 Apr 2024 and a record low of 0.000 % in 06 Jan 2025. Singapore Internet Usage: Social Media Market Share: All Platforms: Youku data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Singapore – Table SG.SC.IU: Internet Usage: Social Media Market Share.

The number of Twitter users in Singapore was forecast to continuously decrease between 2024 and 2028 by in total 0.2 million users (-13.51 percent). After the seventh consecutive decreasing year, the Twitter user base is estimated to reach 1.29 million users and therefore a new minimum in 2028. 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 Philippines and Laos.

（停止更新）互联网使用：社交媒体市场份额：所有平台：Hi5在03-16-2025达0.000%，相较于03-15-2025的0.000%保持不变。（停止更新）互联网使用：社交媒体市场份额：所有平台：Hi5数据按日更新，03-08-2025至03-16-2025期间平均值为0.000%，共9份观测结果。该数据的历史最高值出现于03-12-2025，达0.070%，而历史最低值则出现于03-16-2025，为0.000%。CEIC提供的（停止更新）互联网使用：社交媒体市场份额：所有平台：Hi5数据处于定期更新的状态，数据来源于Statcounter Global Stats，数据归类于全球数据库的新加坡 – Table SG.SC.IU: Internet Usage: Social Media Market Share。

The daily aggregated time-series data used in this study, "Predicting public mental health needs in a crisis using social media indicators: A Singapore big data study" (including actual values and normalised values), are available in the figshare repository. The count of daily emergency room visits data (“IMH Visits”) is available from the corresponding author upon reasonable request.The study can be cited as:Othman, N.A., Panchapakesan, C., Loh, S.B., Zhang, M., Gupta, R.K., Martanto, W., Phang, Y.S., Morris, R.J.T., Loke, W.C., Tan, K.B., Subramaniam, M., Yang, Y. Predicting public mental health needs in a crisis using social media indicators: a Singapore big data study. Sci Rep 14, 23222 (2024). https://doi.org/10.1038/s41598-024-73978-5

The number of LinkedIn users in Singapore was forecast to continuously increase between 2024 and 2028 by in total 0.1 million users (+3.15 percent). After the ninth consecutive increasing year, the LinkedIn user base is estimated to reach 3.25 million users and therefore a new peak in 2028. Notably, the number of LinkedIn users of was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, 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).Find more key insights for the number of LinkedIn users in countries like Indonesia and Malaysia.

This dataset comprises a set of information cascades generated by Singapore Twitter users. Here a cascade is defined as a set of tweets about the same topic. This dataset was collected via the Twitter REST and streaming APIs in the following way. Starting from popular seed users (i.e., users having many followers), we crawled their follow, retweet, and user mention links. We then added those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. With this, we have a total of 184,794 Twitter user accounts. Then tweets are crawled from these users from 1 April to 31 August 2012. In all, we got 32,479,134 tweets. To identify cascades, we extracted all the URL links and hashtags from the above tweets. And these URL links and hashtags are considered as the identities of cascades. In other words, all the tweets which contain the same URL link (or the same hashtag) represent a cascade. Mathematically, a cascade is represented as a set of user-timestamp pairs. Figure 1 provides an example, i.e. cascade C = {< u1, t1 >, < u2, t2 >, < u1, t3 >, < u3, t4 >, < u4, t5 >}. For evaluation, the dataset was split into two parts: four months data for training and the last one month data for testing. Table 1summarizes the basic (count) statistics of the dataset. Each line in each file represents a cascade. The first term in each line is a hashtag or URL, the second term is a list of user-timestamp pairs. Due to privacy concerns, all user identities are anonymized.

According to a survey on social commerce conducted by Rakuten Insight in Singapore in 2025, approximately ** percent of respondents answered that they used TikTok for purchases during the last 12 months. The same survey indicated that around ** percent of Singaporean consumers regularly shopped on social media.

This dataset is crawled from three popular on-line social networks (OSNs), namely, Twitter, Facebook and Foursquare. We collected this dataset as follows. We first gathered a set of Singapore-based Twitter users who declared Singapore as location in their user profiles. From the Singapore-based Twitter users, we retrieve a subset of Twitter users who declared their Facebook or Foursquare accounts in their short bio description. In total, we collected 1,998 Twitter-Facebook user identity pairs (known as TW-FB ground truth matching pairs}, and 3,602 Twitter-Foursquare user identity pairs (known as TW-FQ ground truth matching pairs). To simulate a real-world setting, where a user identity in the source OSN may not have its corresponding matching user identity in the target OSN, we expanded the datasets by adding Twitter, Facebook and Foursquare users who are connected to users in the TW-FB ground truth matching pairs and TW-FQ ground truth matching pairs sets. Note that isolated users who do not have links to other users are removed from the data sets. After collecting the datasets, we extract the following user features using the OSNs' APIs. • Username: The username of the account. • Screen name: The natural name of the user account. It is usually formed using the first and last name of the user. • Profile Image: The thumbnail or image provided by the user to visually present herself. • Network: The relationship links between users.

According to a report published by DataReportal, as of the third quarter of 2024, **** percent of respondents in Singapore stated that their main reason to use social media was to keep in touch with friends and family. Other leading reasons included filling spare time and reading news stories.

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6