100+ datasets found
  1. Highest number of spam e-mails sent daily 2024, by country

    • statista.com
    Updated Dec 9, 2024
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    Statista (2024). Highest number of spam e-mails sent daily 2024, by country [Dataset]. https://www.statista.com/statistics/1270488/spam-emails-sent-daily-by-country/
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    Dataset updated
    Dec 9, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Dec 8, 2024
    Area covered
    Worldwide
    Description

    As of December 8, 2024, China and the United States were the countries with the highest number of spam emails sent within one day worldwide, with around 7.8 billion. Ranking third and fourth were India and the Japan, with around 7.6 billion. Internet and e-mail users around the world Between 2019 and 2024, the number of email users globally increased from 3.9 billion to 4.4 billion. Moreover, this number is expected to increase up to 4.8 billion in 2027. Considering the fact that China and India had the highest number of internet users in the world in 2023, with over 1.2 billion and 1.1 billion users respectively, e-mail usage is less popular in these countries than in the United States or Germany, for example. Most popular online activities in the U.S. Not only did the United States have the highest number of daily emails and spam emails sent as of October 2021, it was actually the most popular online activity among internet users in 2019. In fact, 90.9 percent of respondents said they were email users, more than search users, social network users, or digital video viewers.

  2. Spam: share of global e-mail traffic monthly 2014-2023

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Spam: share of global e-mail traffic monthly 2014-2023 [Dataset]. https://www.statista.com/statistics/420391/spam-email-traffic-share/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2014 - Dec 2023
    Area covered
    Worldwide
    Description

    Spam messages accounted for over **** percent of e-mail traffic in December 2023. Russia generated the largest share of unsolicited spam e-mails in 2022, with **** percent of global spam e-mails originating from the country. Spam worldwide It is almost impossible to think about e-mail without considering the issue of spam, which usually includes billions of promotional e-mails marketers send daily. As of January 2023, the United States had the highest number of spam e-mails sent daily. While many e-mail users believe such content belongs in their spam folder, marketing e-mails are generally harmless if annoying to the user. Malicious spam Phishing e-mails remain one of the primary attack vectors for cybercriminals. On average, around ** percent of businesses worldwide experience four to six successful cyber attacks in one year. Another ** percent said they became victims of more than ** bulk phishing attacks. More than half of the companies said these phishing attacks resulted in consumer or client data breaches.

  3. Spam share of global email traffic 2011-2023

    • statista.com
    Updated Sep 17, 2024
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    Statista (2024). Spam share of global email traffic 2011-2023 [Dataset]. https://www.statista.com/statistics/420400/spam-email-traffic-share-annual/
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    Dataset updated
    Sep 17, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    In 2023, nearly 45.6 percent of all e-mails worldwide were identified as spam, down from almost 49 percent in 2022. While remaining a big part of the e-mail traffic, since 2011, the share of spam e-mails has decreased significantly. In 2023, the highest volume of spam e-mails was registered in May, approximately 50 percent of e-mail traffic worldwide.

  4. z

    Data from: Persuasion Sentences in Spam Email (PerSentSE)

    • zenodo.org
    • portalcienciaytecnologia.jcyl.es
    tsv
    Updated Apr 24, 2025
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    Francisco Jáñez-Martino; Francisco Jáñez-Martino; Alberto Barrón-Cedeño; Alberto Barrón-Cedeño; ROCÍO ALAIZ-RODRÍGUEZ; ROCÍO ALAIZ-RODRÍGUEZ; Víctor González-Castro; Víctor González-Castro (2025). Persuasion Sentences in Spam Email (PerSentSE) [Dataset]. http://doi.org/10.5281/zenodo.14585764
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    tsvAvailable download formats
    Dataset updated
    Apr 24, 2025
    Dataset provided by
    Expert Systems with Applications
    Authors
    Francisco Jáñez-Martino; Francisco Jáñez-Martino; Alberto Barrón-Cedeño; Alberto Barrón-Cedeño; ROCÍO ALAIZ-RODRÍGUEZ; ROCÍO ALAIZ-RODRÍGUEZ; Víctor González-Castro; Víctor González-Castro
    License

    Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
    License information was derived automatically

    Description

    How to Access:

    To access this dataset, please contact Francisco Janez via email at francisco.janez@unileon.es. Access will be granted based on specific requests.

    Purpose:
    The PerSentSE corpus was developed to study persuasive techniques in spam emails. It includes 130 emails randomly selected from the SpamArchive2122 dataset, which contains over 20,000 spam emails in English.

    Methodology:

    • Segmentation: Emails were divided into sentences using the NLTK library.
    • Annotation: Eight persuasive techniques, along with a "non-persuasion" class, were identified. Two expert annotators labeled an initial subset of emails to measure inter-annotator agreement, achieving a final acceptable level (γ = 0.63).

    Corpus Statistics:

    • Total sentences: 1,075
    • Persuasive sentences: 216 (20.1%)

    Persuasion Distribution by Email Sections (Table 7):

    • Subject lines: 35.59% persuasive, with an average of 1.62 techniques.
    • Greeting section: 54.17% persuasive, averaging 1.46 techniques.
    • Email body: 82.46% persuasive, with 5.51 techniques on average.
    • Farewell section: 31.43% persuasive, averaging 1.45 techniques.

    Co-occurrence of Techniques (Figure 2):
    Some persuasive techniques frequently appeared together:

    • Appeal to Fear/Prejudice with Loaded Language: 25 instances.
    • Exaggeration/Minimization with Loaded Language: 24 instances.
    • Appeal to Fear/Prejudice with Exaggeration/Minimization: 20 instances.

    Findings:
    The body section of emails concentrates the highest number of persuasive elements, contrary to earlier studies focusing on subject lines alone. This suggests that spam emails rely heavily on persuasive content in their main text.

  5. Spam e-mail: leading countries of origin of spam 2023

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Spam e-mail: leading countries of origin of spam 2023 [Dataset]. https://www.statista.com/statistics/263086/countries-of-origin-of-spam/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    Worldwide
    Description

    In 2023, Russia ranked first by its share of unsolicited spam e-mails. Overall, **** percent of global spam e-mails originated from IPs in Russia. The United States ranked second, with **** percent. Mainland China followed, accounting for over ** percent of global unsolicited spam e-mails during the measured period.

  6. Email Spam Classification : Cleaned & Feature-Rich

    • kaggle.com
    Updated Jul 25, 2025
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    Gaurav Kumar (2025). Email Spam Classification : Cleaned & Feature-Rich [Dataset]. https://www.kaggle.com/datasets/gauravkumar2525/email-spam-classification-cleaned-and-feature-rich
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 25, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Gaurav Kumar
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    📘 ABOUT

    The Enhanced Email Spam Detection Dataset contains a diverse collection of 10,000 email samples enriched with metadata and linguistic features to support binary spam classification. This cleaned and feature-engineered dataset is ideal for building machine learning models, conducting exploratory data analysis, and developing NLP-based spam filters.

    It is especially useful for data scientists, security researchers, and NLP practitioners interested in understanding spam patterns and creating robust email classification systems.

    Key Features of the Dataset: ✅ Balanced and labeled dataset for spam (1) and non-spam (0) emails ✅ Cleaned structure with no missing values ✅ Includes sender domain, subject lines, and content-based metrics ✅ Engineered features like punctuation ratio, word length, and spam word flags ✅ Suitable for binary classification, model benchmarking, and text pattern analysis

    This dataset provides a strong foundation for spam detection models, enabling pattern discovery across various email features such as urgency cues, promotional language, and sender behavior.

    📂 FILE INFORMATION

    This dataset consists of 10,000 structured email records with various derived features that quantify the likelihood of spam. It has been enhanced by extracting numeric and textual indicators from the email content and headers.

    File Type: CSV Data Rows: 10,000 Data Fields: Email metadata, content metrics, keyword flags, and label column

    📊 COLUMNS DESCRIPTION

    Column NameDescription
    idUnique identifier for each email.
    labelBinary label (1 = spam, 0 = not spam).
    subjectSubject line of the email.
    sender_domainDomain of the email sender.
    has_urlIndicates if the email contains a URL.
    email_lengthNumber of characters in the email.
    word_countTotal word count in the email.
    char_countTotal character count excluding spaces.
    digit_countNumber of numeric digits in the email.
    uppercase_wordsNumber of fully uppercase words.
    exclamationsNumber of exclamation marks used.
    avg_word_lengthAverage length of words in the email.
    punc_ratioRatio of punctuation marks to characters.
    has_noreplyIndicates presence of 'noreply' in sender or text.
    has_freeBinary flag for the word "free".
    has_winBinary flag for the word "win".
    has_winnerBinary flag for the word "winner".
    has_clickBinary flag for the word "click".
    has_offerBinary flag for promotional "offer".
    has_urgentBinary flag for urgency words like "urgent".
    has_limitedIndicates limited-time offers or phrases.
    has_buyBinary flag for commercial intent ("buy").
    has_nowIndicates time-sensitive prompts ("now").
    has_moneyBinary flag for monetary terms ("money").
  7. t

    Data from: Spam Mails Dataset

    • dbrepo.datalab.tuwien.ac.at
    Updated Apr 19, 2025
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    Bernal, Nicolas (2025). Spam Mails Dataset [Dataset]. http://doi.org/10.82556/bexb-5283
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    Dataset updated
    Apr 19, 2025
    Authors
    Bernal, Nicolas
    Time period covered
    2025
    Description

    Preprocessed data derived from the "spam-mails" dataset, containing email messages labeled as spam or ham. Each record includes a unique identifier from the original dataset and an experiment_id indicating its assignment to a specific data split (training, validation, or test) used in this experiment. The email content has been lemmatized and cleaned to remove noise such as punctuation, special characters, and stopwords, ensuring consistent input for embedding and model training. Original data source: https://www.kaggle.com/datasets/venky73/spam-mails-dataset

  8. t

    Spam Mails Dataset - FAIR experiment

    • test.researchdata.tuwien.ac.at
    application/x-hdf5 +3
    Updated Apr 25, 2025
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    Nicolas Bernal; Nicolas Bernal; Nicolas Bernal; Nicolas Bernal (2025). Spam Mails Dataset - FAIR experiment [Dataset]. http://doi.org/10.70124/0e1sf-saz86
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    application/x-hdf5, png, txt, csvAvailable download formats
    Dataset updated
    Apr 25, 2025
    Dataset provided by
    TU Wien
    Authors
    Nicolas Bernal; Nicolas Bernal; Nicolas Bernal; Nicolas Bernal
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Context

    The Spam Mail dataset is a collection of 5.171 emails that have been classified as spam or ham (non-spam). This dataset was originally created in 2006 for research purposes in the field of spam detection and filtering using machine learning techniques, specifically a Naive Bayes classifier as described in the paper "Spam Filtering with Naive Bayes - Which Naive Bayes?" by Metsis, Androutsopoulos, and Paliouras.
    The data was created using mainly the inbox of 6 users of the company "Enron" for the "ham" emails, and the "spam" emails were collected from various sources, including the SpamAssassin corpus, the Honeypot project, the spam collection of Bruce Guenter, and spam collected by the authors themselves.
    The emails were preprocessed to remove any html tags, and emails with non-latin characters were removed to avoid any possible bias since all "ham" emails are written with latin characters.
    The original data can be found in CSV format on Kaggle at: https://www.kaggle.com/datasets/venky73/spam-mails-dataset/data

    Project description

    In this project we will use the Spam Mail dataset to train a Neural Network model to classify emails as spam or ham. The dataset will be further preprocessed to remove any unnecessary characters like stopwords and punctuation.
    The emails will also be tokenized and converted into a format suitable for training the model, but this last step will be performed in the code itself so it is not included in the dataset.

    Files

    In this repository you will find the following files:
    - README.md: Project overview, dataset source, structure, and dependency information.
    - confusion_matrix.png: A confusion matrix that shows the performance of the model on the test set.
    - evaluation_metrics.txt: Text summary of evaluation metrics: accuracy, precision, recall, and F1-score.
    - test_predictions.csv: A CSV file that contains the predictions of the model on the test set.
    - top_spam_words.png: A bar chart showing the top 10 most frequent words in correctly predicted spam emails.
    - spam_classifier.h5: The trained model file, which can be used to make predictions on new emails.
  9. h

    all-scam-spam

    • huggingface.co
    Updated Sep 2, 2002
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    Fred Zhang (2002). all-scam-spam [Dataset]. https://huggingface.co/datasets/FredZhang7/all-scam-spam
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 2, 2002
    Authors
    Fred Zhang
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    This is a large corpus of 42,619 preprocessed text messages and emails sent by humans in 43 languages. is_spam=1 means spam and is_spam=0 means ham. 1040 rows of balanced data, consisting of casual conversations and scam emails in ≈10 languages, were manually collected and annotated by me, with some help from ChatGPT.

      Some preprcoessing algorithms
    

    spam_assassin.js, followed by spam_assassin.py enron_spam.py

      Data composition
    
    
    
    
    
    
    
    
      Description
    

    To make the text… See the full description on the dataset page: https://huggingface.co/datasets/FredZhang7/all-scam-spam.

  10. Spam or real Email dataset

    • kaggle.com
    Updated Jul 4, 2025
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    The citation is currently not available for this dataset.
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jul 4, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Abdul Wadood
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    📄 Dataset Description This dataset contains 5,000 sample emails labeled as either "spam" or "ham" (not spam). It is designed to help build and evaluate machine learning models for spam detection using natural language processing (NLP) techniques.

    The data is synthetically generated to reflect realistic spam and ham email patterns, including promotional content, phishing alerts, reminders, and casual conversations.

    📁 Files Included train.csv Contains 4,000 labeled email samples used to train a model. Columns:

    label: Spam classification (spam or ham)

    text: The content of the email

    test.csv Contains 1,000 unlabeled email samples used for testing/prediction. Columns:

    text: The content of the email

    Note: You can evaluate your model on this test set using a private test_labels.csv if needed.

    ✅ Use Cases Binary text classification (Spam vs. Ham)

    NLP preprocessing and vectorization (TF-IDF, CountVectorizer, embeddings)

    Model training (Naive Bayes, Logistic Regression, SVM, Transformers)

    Evaluation metrics (Accuracy, Precision, Recall, F1-score)

    📊 Suggested Evaluation Workflow Train model on train.csv

    Predict on test.csv

    Evaluate predictions if test_labels.csv is available (optional)

  11. D

    E Mail Spam Filter Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
    + more versions
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    Dataintelo (2025). E Mail Spam Filter Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-e-mail-spam-filter-market
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    pptx, csv, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    E-Mail Spam Filter Market Outlook



    The global market size for e-mail spam filters is poised to grow from approximately USD 2.84 billion in 2023 to an estimated USD 5.97 billion by 2032, with a robust compound annual growth rate (CAGR) of 8.5%. This growth is driven by increasing cyber threats and the rising importance of securing communication channels.



    One of the primary growth factors for the e-mail spam filter market is the escalating number of cyberattacks and phishing scams. These attacks often infiltrate via spam emails, making it critical for organizations to implement robust spam filters to protect sensitive information. The sophistication of spam email tactics has evolved, necessitating advanced filtering solutions that can detect and block such threats effectively. Consequently, the demand for dynamic and intelligent spam filtering systems is on the rise.



    Furthermore, the growing regulatory demands for data protection and privacy act as significant drivers for this market. Regulations such as GDPR in Europe and CCPA in California mandate stringent measures to protect users' data, including the prevention of spam and phishing emails. Compliance with these regulations often requires the deployment of advanced spam filtering technologies, thereby propelling market growth. Companies are increasingly investing in these solutions to avoid hefty penalties and maintain customer trust.



    In addition to spam filters, Email Protection Software plays a crucial role in safeguarding communication channels from a myriad of cyber threats. These software solutions provide comprehensive protection by integrating features such as encryption, data loss prevention, and threat intelligence. With the increasing sophistication of cyberattacks, organizations are turning to email protection software to ensure the confidentiality and integrity of their communications. This software not only helps in blocking spam but also offers advanced threat detection capabilities, making it an indispensable tool for modern businesses aiming to secure their email infrastructure.



    Another crucial factor contributing to the market's expansion is the increasing adoption of cloud-based services. Cloud computing offers scalable solutions that can be easily integrated with existing email systems, providing efficient spam filtering capabilities without the need for significant upfront investments in hardware. This flexibility and cost-effectiveness make cloud-based spam filters particularly attractive to small and medium enterprises (SMEs), further driving market growth.



    Regionally, North America holds a significant share of the e-mail spam filter market, owing to the high adoption of advanced technologies and the presence of major industry players. The Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, driven by the rapid digital transformation and increasing cyber threats in emerging economies like India and China. The stringent regulatory environment in Europe also ensures steady demand for spam filter solutions in this region.



    Component Analysis



    The e-mail spam filter market can be broadly segmented into two main components: software and services. The software segment encompasses the actual spam filtering applications that can be installed and integrated into email systems. These software solutions range from basic spam filters to advanced machine learning-based systems that can adapt to new threats. The demand for software solutions is driven by their ability to provide real-time protection against spam and phishing attacks, ensuring the security of organizational communication channels.



    On the other hand, the services segment includes managed services, consulting, and support services provided by vendors. Managed services are particularly popular among organizations that lack the in-house expertise to manage and update spam filters. These services often include regular updates, monitoring, and management of the spam filtering systems, ensuring optimal performance and protection. Consulting services help organizations choose the right spam filtering solutions and implement them effectively, while support services provide ongoing assistance to address any issues that may arise.



    The software segment is anticipated to hold a larger market share due to the increasing preference for advanced spam filtering solutions that can be customize

  12. Average results by industry

    • gazellenstag.com
    • getresponse.com
    Updated Nov 15, 2021
    + more versions
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    GetResponse (2021). Average results by industry [Dataset]. https://gazellenstag.com/?_=%2Fbaza-wiedzy%2Freports%2Femail-marketing-benchmarks%231RU4ijafpP9N9cmDe60g90kvHXva5DsJf6Q%3D
    Explore at:
    Dataset updated
    Nov 15, 2021
    Dataset authored and provided by
    GetResponse
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This table shows the email marketing benchmarks across different industries. For each sector, we've identified the average email open rate, CTR, CTOR, unsubscribe rate, and spam complaint rate. Thanks to this data, you can compare your email marketing campaigns' results with other companies from your vertical. It's beneficial to look at the industry-level data breakdown as each sector is different. The target audience, type of communication, and competition levels all play a role in how high the average email marketing statistics are.

  13. Spam: share of global email traffic 2014-2021

    • digi.czlib.net
    Updated Apr 30, 2019
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    Statista (2019). Spam: share of global email traffic 2014-2021 [Dataset]. http://digi.czlib.net/interlibSSO/goto/2/++9rs-shrs-9bnl/statistics/420391/spam-email-traffic-share/
    Explore at:
    Dataset updated
    Apr 30, 2019
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2014 - Mar 2021
    Area covered
    Worldwide
    Description

    Spam messages accounted for 45.1 percent of e-mail traffic in March 2021. During the most recently measured period, Russia generated the largest share of unsolicited spam e-mails with 23.52 percent of global spam volume. Despite its ubiquity, the global e-mail spam rate has actually been decreasing: the global annual spam e-mail rate in 2018 was 55 percent, down from 69 percent in 2012. Spam e-mail It is almost impossible to think about e-mail without considering the issue of spam. In 2019, 293.6 billion e-mails were sent and received on a daily basis. This includes billions of promotional e-mails sent by marketers every day. Whilst many e-mail users believe such content belongs in their spam folder, marketing e-mails are generally harmless, if annoying to the user. In 2018, the spam placement rate of commercial e-mails had declined to nine percent, down from 14 percent in 2017.

    Malicious spam Not all spam are benign promotional e-mails though. A significant portion of spam messages are of a more malicious nature, aiming to damage or hijack user systems. The most common variants of malicious spam worldwide include trojans, spyware, and ransomware.

  14. Average results by country

    • getresponse.com
    Updated Mar 14, 2016
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    GetResponse (2016). Average results by country [Dataset]. https://www.getresponse.com/resources/reports/email-marketing-benchmarks
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    Dataset updated
    Mar 14, 2016
    Dataset authored and provided by
    GetResponse
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    What are the average email marketing results in different countries? Here’s what we’ve found.

  15. S

    Spam Blocking Software Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated May 23, 2025
    + more versions
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    Archive Market Research (2025). Spam Blocking Software Report [Dataset]. https://www.archivemarketresearch.com/reports/spam-blocking-software-564197
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    pdf, doc, pptAvailable download formats
    Dataset updated
    May 23, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global spam blocking software market is experiencing robust growth, driven by the escalating volume of spam emails and phishing attempts targeting individuals and organizations. While precise figures for market size and CAGR are unavailable in the provided data, a reasonable estimate can be made based on industry trends. Considering the increasing sophistication of spam techniques and the rising reliance on email for both personal and business communications, a conservative market size estimate for 2025 is $5 billion. Given the ongoing demand for robust email security solutions, a projected Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033 seems plausible. This growth is fuelled by several factors, including the rise of cloud-based email security solutions, increasing adoption of artificial intelligence (AI) and machine learning (ML) in spam filtering, and the growing awareness of the financial and reputational risks associated with unfiltered spam and phishing emails. Furthermore, stringent data privacy regulations are driving organizations to seek advanced spam blocking solutions to safeguard sensitive customer data. This market segment is highly competitive, with established players like McAfee and Kaspersky competing alongside specialized providers such as SpamTitan and Truecaller. The market is witnessing innovation in spam detection techniques, including advanced heuristics, Bayesian filtering, and behavioral analysis. The segment is also seeing a shift towards integrated security suites, offering spam blocking alongside other email security features such as anti-virus and data loss prevention. Future growth will likely be influenced by developments in AI-powered spam detection, the integration of spam blocking into broader cybersecurity platforms, and the increasing demand for solutions that can effectively address sophisticated spam and phishing campaigns targeting mobile devices. The market is segmented by deployment model (cloud-based, on-premises), by organization size (SMEs, large enterprises), and by geographical region. Understanding these segments is critical for businesses seeking to capitalize on the opportunities within this rapidly expanding market.

  16. Daily spam volume worldwide 2020-2021

    • statista.com
    Updated Jun 26, 2023
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    Statista (2023). Daily spam volume worldwide 2020-2021 [Dataset]. https://www.statista.com/statistics/1270424/daily-spam-volume-global/
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    Dataset updated
    Jun 26, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Oct 2020 - Sep 2021
    Area covered
    Worldwide
    Description

    Between October 2020 and September 2021, global daily spam volume reached its highest point in July 2021, with almost 283 billion spam emails from a total of 336.41 billion sent emails. As of August 2021, this number dropped to 65.50 billion. Towards September the average spam volume again saw an increase of 36 percent, reaching 88.88 billion from the total of 105.67 billion emails sent worldwide. The country, where the most emails were sent, was the U.S..

  17. D

    Email Anti-spam Software Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). Email Anti-spam Software Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-email-anti-spam-software-market
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    pptx, csv, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Email Anti-spam Software Market Outlook



    The global email anti-spam software market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach nearly USD 4.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.7% during the forecast period. The significant growth factor driving this market is the increasing volume of spam emails, which has heightened the demand for robust email security solutions.



    One of the primary growth factors for the email anti-spam software market is the proliferation of spam and phishing attacks. As email remains a critical communication tool for both individuals and businesses, the rise in cyber threats has led to a greater need for advanced spam filtering solutions. Organizations are seeking sophisticated software capable of detecting and blocking malicious emails, thereby safeguarding sensitive information and protecting against data breaches. This demand is further fueled by regulatory requirements mandating stringent data protection measures.



    Another key growth factor is the increasing adoption of cloud-based solutions. Cloud deployment offers numerous advantages, including scalability, ease of integration, and cost-effectiveness. As more businesses migrate their operations to the cloud, the demand for cloud-based email anti-spam solutions is surging. These solutions are particularly appealing to small and medium enterprises (SMEs), which may lack the resources to invest in extensive on-premises infrastructure. Cloud solutions provide these organizations with robust security features, ensuring their email systems remain secure and compliant.



    Technological advancements in artificial intelligence (AI) and machine learning (ML) are also propelling market growth. Modern email anti-spam software leverages AI and ML algorithms to enhance the accuracy and efficiency of spam detection. These technologies enable the software to learn from patterns and behaviors, improving its ability to identify new and sophisticated spam tactics. The continuous evolution of AI and ML technologies promises to further strengthen the capabilities of email anti-spam solutions, driving their adoption across various sectors.



    The rise of Cloud-based Email Security solutions is revolutionizing the way organizations approach email protection. By leveraging cloud infrastructure, these solutions offer enhanced flexibility and scalability, allowing businesses to adapt quickly to changing security landscapes. Cloud-based systems are particularly advantageous for organizations with distributed teams, as they provide seamless access to security features from any location. Furthermore, they reduce the burden of maintaining on-premises hardware, enabling IT teams to focus on strategic initiatives rather than routine maintenance. As cyber threats evolve, cloud-based email security solutions continuously update to provide the latest protection, ensuring that organizations remain one step ahead of potential attacks. This adaptability and ease of use are driving more companies to transition to cloud-based models, aligning with broader digital transformation trends.



    Regionally, North America holds a substantial share of the email anti-spam software market. The presence of leading market players, coupled with high adoption rates of advanced cybersecurity solutions, drives this dominance. Additionally, stringent regulatory frameworks in the United States and Canada emphasize the need for robust email security, further boosting market growth in the region. Europe follows closely, with the General Data Protection Regulation (GDPR) playing a pivotal role in ensuring data security and privacy, thereby driving the demand for email anti-spam software.



    Component Analysis



    The email anti-spam software market is segmented by components into software and services. The software segment dominates the market, driven by the continuous need for effective spam detection and email security solutions. The software is designed to identify and block spam emails before they reach the userÂ’s inbox, leveraging a combination of filters, algorithms, and databases. This segment is witnessing continuous innovation, with vendors incorporating advanced AI and ML features to enhance detection accuracy and efficiency.



    Software solutions are further categorized into standalone and integrated solutions. Standalone software is specifically designed to target spam emails, while integrated solutions are

  18. E

    E-Mail Spam Filter Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated May 16, 2025
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    Data Insights Market (2025). E-Mail Spam Filter Report [Dataset]. https://www.datainsightsmarket.com/reports/e-mail-spam-filter-540969
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    pdf, doc, pptAvailable download formats
    Dataset updated
    May 16, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global email spam filter market is experiencing robust growth, driven by the escalating volume of spam emails and the increasing sophistication of phishing and malware attacks targeting individuals and organizations alike. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% between 2025 and 2033, reaching approximately $45 billion by 2033. This growth is fueled by several key factors. The widespread adoption of cloud-based solutions offers scalability, cost-effectiveness, and enhanced security features, contributing significantly to market expansion. Furthermore, stringent data privacy regulations across various regions are compelling businesses and government entities to invest heavily in robust email security solutions. The increasing prevalence of ransomware attacks and the associated financial losses are further driving demand for sophisticated email spam filters. Segmentation reveals a strong preference for cloud-based solutions over on-premises deployments, reflecting the overall shift towards cloud computing. The enterprise segment holds the largest market share, driven by the need for comprehensive security measures in large organizations. Geographically, North America and Europe currently dominate the market, although the Asia-Pacific region is poised for significant growth due to increasing internet penetration and rising awareness of cybersecurity threats. However, the market faces certain restraints. The high initial investment required for implementing advanced spam filtering solutions can be a barrier to entry for small and medium-sized enterprises (SMEs). Furthermore, the constant evolution of spam techniques necessitates continuous updates and upgrades to email filter technology, leading to ongoing operational costs. The complexity of managing and maintaining email security solutions can also deter some organizations, particularly those with limited IT resources. Despite these challenges, the overall market outlook remains positive, fueled by the persistent threat of spam and the growing need for robust email security. Key players like TitanHQ, Hornetsecurity, and others are leveraging technological advancements, such as AI and machine learning, to enhance the effectiveness of their solutions and cater to the evolving needs of the market. Competition is intense, driving innovation and price optimization within the sector.

  19. Number of newsletters per week

    • getresponse.com
    Updated Mar 14, 2016
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    GetResponse (2016). Number of newsletters per week [Dataset]. https://www.getresponse.com/resources/reports/email-marketing-benchmarks
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    Dataset updated
    Mar 14, 2016
    Dataset authored and provided by
    GetResponse
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    What’s the right email frequency? What’s the potential increase in the number of conversions your email campaigns generate if you add an extra message to your schedule? The data in this table should help you find the right answers.

  20. h

    uplimit-week-1-synthetic-spam-email-data-filtered-quality

    • huggingface.co
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    Boston Lee, uplimit-week-1-synthetic-spam-email-data-filtered-quality [Dataset]. https://huggingface.co/datasets/boleeproofpoint/uplimit-week-1-synthetic-spam-email-data-filtered-quality
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    Authors
    Boston Lee
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    boleeproofpoint/uplimit-week-1-synthetic-spam-email-data-filtered-quality dataset hosted on Hugging Face and contributed by the HF Datasets community

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Statista (2024). Highest number of spam e-mails sent daily 2024, by country [Dataset]. https://www.statista.com/statistics/1270488/spam-emails-sent-daily-by-country/
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Highest number of spam e-mails sent daily 2024, by country

Explore at:
7 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Dec 9, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Dec 8, 2024
Area covered
Worldwide
Description

As of December 8, 2024, China and the United States were the countries with the highest number of spam emails sent within one day worldwide, with around 7.8 billion. Ranking third and fourth were India and the Japan, with around 7.6 billion. Internet and e-mail users around the world Between 2019 and 2024, the number of email users globally increased from 3.9 billion to 4.4 billion. Moreover, this number is expected to increase up to 4.8 billion in 2027. Considering the fact that China and India had the highest number of internet users in the world in 2023, with over 1.2 billion and 1.1 billion users respectively, e-mail usage is less popular in these countries than in the United States or Germany, for example. Most popular online activities in the U.S. Not only did the United States have the highest number of daily emails and spam emails sent as of October 2021, it was actually the most popular online activity among internet users in 2019. In fact, 90.9 percent of respondents said they were email users, more than search users, social network users, or digital video viewers.

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