In March 2024, close to 35.9 million unique global visitors visited Blogger.com, down from 38.4 million visitors in January of the same year. Blogger is a blogging and content management system which was acquired by Google in 2003.

A global study among bloggers conducted in July and August 2023 found that around 76 percent reported having published how-to articles throughout the 12 months preceding the survey. Approximately 55 percent said they posted lists.

Introduction

Blogging Statistics: Blogging remains a pivotal element in digital content strategies, with over 600 million blogs among 1.9 billion websites globally. WordPress alone powers more than 43% of all websites, hosting over 60 million blogs and facilitating approximately 70 million new posts each month. In the United States, the blogging community has expanded to over 32.7 million active bloggers as of 2022. Globally, bloggers publish around 3 billion posts annually, equating to over 8.2 million posts daily.

The influence of blogs is substantial, with 77% of internet users regularly reading blog content. Incorporating relevant images can enhance blog views by 94%, and posts with seven or more images are 2.3 times more likely to yield strong results. Furthermore, 70% of consumers prefer learning about companies through articles rather than advertisements, highlighting the trust and engagement blogs foster.

For businesses, blogging offers significant advantages: companies with active blogs experience 55% more website visitors and generate 67% more monthly leads compared to those without. These statistics underscore blogging's role as a cost-effective and impactful tool for enhancing brand visibility and driving audience engagement.

With internet access, anyone can start a blog and reach a global audience through social media. In this article, we'll explore blogging statistics in more detail.

Check out the latest blogging statistics for 2025, such as average blog length, blog readership statistics, blog traffic stats, average blogging income and more.

As of August 2023, approximately ** percent of bloggers reporting strong results surveyed worldwide said they spent, on average, over six hours on a typical blog post. Around ** percent spent between **** and *** hours on a blog entry.

This dataset contains statistics on the usage patterns of the official City of Seattle blogs. 2011 - Present. It replaces an internal spreadsheet. The information is used to compare pageview activity between blogs. Not all urls represented in the datasheet are active. Active blogs would have pageviews greater than zero for the most recent month uploaded. The format of this dataset is likely to change over time per approved requests to track additional automated information.

As of August 2023, more than **** out of 10 bloggers surveyed worldwide reported including images in their typical blog posts. Half of the respondents said they added statistics to the entries, while contributor quotes rounded up the top three, mentioned by over ********* of the interviewees.

As of August 2023, nearly one-quarter (or ** percent) of bloggers surveyed worldwide reported posting blog entries weekly. Around ** percent did it several times per month, while ** percent said they blogged monthly.

The Blog-1K corpus is a redistributable authorship identification testbed for contemporary English prose. It has 1,000 candidate authors, 16K+ posts, and a pre-defined data split (train/dev/test proportional to ca. 8:1:1). It is a subset of the Blog Authorship Corpus from Kaggle. The MD5 for Blog-1K is '0a9e38740af9f921b6316b7f400acf06'.

1. Preprocessing

We first filter out texts shorter than 1,000 characters. Then we select one thousand authors whose writings meet the following criteria:
- accumulatively at least 10,000 characters,
- accumulatively at most 49,410 characters,
- accumulatively at least 16 posts,
- accumulatively at most 40 posts, and
- each text has at least 50 function words found in the Koppel512 list (to filter out non-English prose).

Blog-1K has three columns: 'id', 'text', and 'split', where 'id' corresponds to its parent corpus.

2. Statistics

Its creation and statistics can be found in the Jupyter Notebook.

3. Usage

import pandas as pd

df = pd.read_csv('blog1000.csv.gz', compression='infer')

# read in training data
train_text, train_label = zip(*df.loc[df.split=='train'][['text', 'id']].itertuples(index=False))

4. License
All the materials is licensed under the ISC License.

5. Contact
Please contact its maintainer for questions.

Source:

Krisztian Buza Budapest University of Technology and Economics buza '@' cs.bme.hu http://www.cs.bme.hu/~buza

You can download a zip file from https://archive.ics.uci.edu/ml/datasets/BlogFeedback

Data Set Information:

This data originates from blog posts. The raw HTML-documents of the blog posts were crawled and processed.

The prediction task associated with the data is the prediction of the number of comments in the upcoming 24 hours.

In order to simulate this situation, we choose a basetime (in the past) and select the blog posts that were published at most 72 hours before the selected base date/time. Then, we calculate all the features of the selected blog posts from the information that was available at the basetime, therefore each instance corresponds to a blog post. The target is the number of comments that the blog post received in the next 24 hours relative to the base time.

In the train data, the base times were in the years 2010 and 2011. In the test data the base times were in February and March 2012.

This simulates the real-world situation in which training data from the past is available to predict events in the future.

The train data was generated from different base times that may temporally overlap.

Therefore, if you simply split the train into disjoint partitions, the underlying time intervals may overlap.

Therefore, you should use the provided, temporally disjoint train and test splits in order to ensure that the evaluation is fair.

** Attribute Information:**

1...50: Average, standard deviation, min, max and median of the Attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared. For example, myblog.blog.org would be the source of the post myblog.blog.org/post_2010_09_10

51: Total number of comments before basetime 52: Number of comments in the last 24 hours before the base time 53: Let T1 denote the datetime 48 hours before basetime, Let T2 denote the datetime 24 hours before basetime. This attribute is the number of comments in the time period between T1 and T2 54: Number of comments in the first 24 hours after the publication of the blog post, but before basetime 55: The difference of Attribute 52 and Attribute 53 56...60: The same features as the attributes 51...55, but features 56...60 refer to the number of links (trackbacks), while features 51...55 refer to the number of comments. 61: The length of time between the publication of the blog post and base time 62: The length of the blog post 63...262: The 200 bag of words features for 200 frequent words of the text of the blog post 263...269: binary indicator features (0 or 1) for the weekday (Monday...Sunday) of the basetime 270...276: binary indicator features (0 or 1) for the weekday (Monday...Sunday) of the date of publication of the blog post 277: Number of parent pages: we consider a blog post P as a parent of blog post B, if B is a reply (trackback) to blog post P. 278...280: Minimum, maximum, average number of comments that the parents received 281: The target: the number of comments in the next 24 hours (relative to base time)

** Relevant Papers:**

Buza, K. (2014). Feedback Prediction for Blogs. In Data Analysis, Machine Learning and Knowledge Discovery (pp. 145-152). Springer International Publishing (http://cs.bme.hu/~buza/pdfs/gfkl2012_blogs.pdf).

This blog post was posted by Paula Braun on January 16, 2015.

This statistic illustrates Tumblr.com's cumulative total blogs from May 2011 to April 2020. As of that month, the social networking site had over 496 million blog accounts, up from 463.5 million in the corresponding period of the previous year. Tumblr – further information Founded in February 2007, Tumblr is a microblogging website and social media platform that is now owned by Yahoo. The website allows users to post images, videos, links and other media content to a short-form blog. Users can follow each other as well as access and post blog content via the platform’s user dashboard. As of April 2019, over 171.5 billion Tumblr posts have been generated on the social network. The social network accounts for less than one percent of total social media website visits in the United States, and has a user base consisting mainly of teen and young adult internet users.

Despite the relatively low audience reach, Tumblr is a popular platform for online fandom discussions regarding music, movies and TV shows. Similar to Instagram, Tumblr was also set to take advantage of social media marketing by providing an ideal platform for visually-oriented brands in the retail and media sector. However, Tumblr adoption among marketers has been declining in recent years, and as such, Tumblr remains a niche marketing channel.

As of August 2023, more than **** out of 10 bloggers surveyed worldwide reported using social media to promote their blog posts. E-mail marketing and search engine optimization (SEO) followed, each mentioned by about ********** of respondents.

Treasury's official blog, featuring blog posts from Treasury's senior officials and staff sharing news, announcements and information about the work done at the Treasury Department.

Abstract: BlogCatalog is the social blog directory which manages the bloggers and their blogs.Number of Nodes:10,312Number of Edges:333,983Missing Values?noSource:Nitin Agarwal+, Xufei Wang*, Huan Liu*+ Department of Information Science, University of Arkansas at Little Rock. E-mail:nxagarwal@ualr.edu* School of Computing, Informatics and Decision Systems Engineering, Arizona State University. E-mail: huan.liu@asu.edu, xufei.wang@asu.eduData Set Information:2 files are included:1. nodes.csv-- it's the file of all the users. This file works as a dictionary of all the users in this data set. It's useful for fast reference. It contains all the node ids used in the dataset.2. edges.csv-- this is the friendship network among the bloggers. The blogger's friends are represented using edges. Here is an example.1,2This means blogger with id "1" is friend with blogger id "2".Attribute Information:This is the data set crawled on July, 2009 from BlogCatalog ( http://www.blogcatalog.com ). BlogCatalog is a social blog directory website. This contains the friendship network crawled. For easier understanding, all the contents are organized in CSV file format.-. Basic statisticsNumber of bloggers : 88,784Number of friendship pairs: 4,186,390Relevant Papers:Nitin Agarwal and Huan Liu. ”Modeling and Data Mining in Blogosphere”, Synthesis Lectures on Data Mining and Knowledge Discovery #1, Morgan & Claypool Publishers, Robert Grossman (Editor), August 2009. ISBN: 9781598299083 (paperback) ISBN: 9781598299090 (ebook) Nitin Agarwal, Magdiel Galan, Huan Liu, and Shankar Subramanya. WisColl: Collective Wisdom based Blog Clustering. Journal of Information Science, 180(1): 39-61, January, 2010. Nitin Agarwal, Huan Liu, Sudheendra Murthy, Arunabha Sen, and Xufei Wang. A Social Identity Approach to Identify Familiar Strangers in a Social Network. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media (ICWSM09), pp. 2 - 9, May 17-20, 2009. San Jose, California. Nitin Agarwal, Huan Liu, Sudheendra Murthy, Arunabha Sen, and Xufei Wang. "A Social Identity Approach to Identify Familiar Strangers in a Social Network", 3rd International AAAI Conference on Weblogs and Social Media (ICWSM09), pp. 2 - 9, May 17-20, 2009. San Jose, California.

The popularity of science blogging has increased in recent years, but the number of academic scientists who maintain regular blogs is limited. The role and impact of science communication blogs aimed at general audiences is often discussed, but the value of science community blogs aimed at the academic community has largely been overlooked. Here, we focus on our own experiences as bloggers to argue that science community blogs are valuable to the academic community. We use data from our own blogs (n = 7) to illustrate some of the factors influencing reach and impact of science community blogs. We then discuss the value of blogs as a standalone medium, where rapid communication of scholarly ideas, opinions, and short observational notes can enhance scientific discourse, and discussion of personal experiences can provide indirect mentorship for junior researchers and scientists from underrepresented groups. Finally, we argue that science community blogs can be treated as a primary source and provide some key points to consider when citing blogs in peer-reviewed literature.

TUAW Dataset Statistics.

A complete list of live websites using the Feedjit Live Blog Stats technology, compiled through global website indexing conducted by WebTechSurvey.

In NLP Centre, dividing text into sentences is currently done with

a tool which uses rule-based system. In order to make enough training

data for machine learning, annotators manually split the corpus of contemporary text

CBB.blog (1 million tokens) into sentences.

Each file contains one hundredth of the whole corpus and all data were

processed in parallel by two annotators.

The corpus was created from ten contemporary blogs:

hintzu.otaku.cz

modnipeklo.cz

bloc.cz

aleneprokopova.blogspot.com

blog.aktualne.cz

fuchsova.blog.onaidnes.cz

havlik.blog.idnes.cz

blog.aktualne.centrum.cz

klusak.blogspot.cz

myego.cz/welldone

Browse the most interesting pieces of data and statistics from around the world of WordPress. Use them whenever you’re working on a new article, blog post, infographic, or whatever else you have in store.