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****From April 2023 free access to Twitter/X stopped - Mozdeh still works for YouTube and Reddit****

A free Windows program for keyword, issue, time series, sentiment, gender and content analyses of short texts, imported or downloaded from YouTube or Reddit. *download*

For updates, follow @Mozdeh4

• Free online course 1: Introduction to YouTube analysis with Mozdeh. [There is also a Twitter part no longer works]
• Free online course 2: Word association thematic analysis with Mozdeh. See also the Word Association Thematic Analysis information page.
• Presentation files summarising four Covid-19 studies.
• There are two variants of Mozdeh, Standard and Compulsory Anonymisation. The second variant automatically anonymises YouTube and Reddit usernames and is intended for privacy protection, especially for students. The anonymisation is optional in the main version.

Gather or import texts

• Gather comments on YouTube videos - about a million per day. Video about downloading videos.
• Gather comments from subreddits on Reddit.
• Import academic citations, TripAdvisor reviews, Reddit comments, or other formatted or unformatted texts: see the Import Data button in the Startup Wizard.

Analyse

These options work for all languages with spaces between words. Special instructions for Japanese and Chinese.

• Search or filter the texts gathered with keyword queries, sentiment strength, country [Twitter only], retweets/likes, and/or author gender (male, female, nonbinary).
• Mine word associations to get insights into important issues within your texts by topic, gender, country, time, popularity, and sentiment.
• **Word Association Thematic Analysis**: to discover themes in your text. Video and Book about the method.
• Researching politics on social media with Mozdeh - video by Dr Rosalynd Southern.
• Search by sentiment, measure the average sentiment for a query, or find terms associating with positive or negative sentiment.
• Mine gender differences in the texts.
• Find the average citation/like count for texts matching a query/gender/sentiment (includes confidence intervals).
• Draw time series graphs of the volume of posts in your topic or the percentage of texts matching any query in your topic. See also examples of time series analyses.
• Create networks of users from their texts.
• Create time series graphs for an individual user in your collection (thanks to Kim Holmberg for these instructions).
• Draw a time series graph of the average positive and negative sentiment strength of texts overall or for texts matching a query in your topic (see the bottom of the main time series graphs page).
• Find words with a sudden frequency increase (i.e., keyword spike detection) in the data.
• Generate topic models to find the main topics of discussion in the texts.

Save selected texts

• Save a random sample of texts for a content analysis. If you already have a sample and need to randomise it you could use Excel.

Examples of papers and books written with Mozdeh

Thelwall, M. (2021). Word association thematic analysis: A social media text exploration strategy. San Rafael, CA: Morgan & Claypool. [A method to analyse text for themes with Mozdeh.]

Thelwall, M. (2018). Social web text analytics with Mozdeh. University of Wolverhampton. [free short overview book]

Mozdeh was used to collect and analyse data for the following.

Thelwall, M., Thelwall, S. & Fairclough, R. (2021). Male, female, and nonbinary differences in UK Twitter self-descriptions: A fine-grained systematic exploration. Journal of Data and Information Science, 6(2), 1-27.

Thelwall, M., Makita, M., Mas-Bleda, A., & Stuart, E. (2021). “My ADHD hellbrain”: A Twitter data science perspective on a behavioural disorder. Journal of Data and Information Science, 6(1).

Thelwall, S. & Thelwall, M. (2020). Anthropomorphizing atopy: Tweeting about eczema. Journal of the Dermatology Nurses' Association, 12(2), 74-77.

Potts, G., & Radford, D. R. (2019). #Teeth&Tweets: the reach and reaction of an online social media oral health promotion campaign. British Dental Journal, 227(3), 217-222.

Thelwall, M. (2018). Can museums find male or female audiences online with YouTube? Aslib Journal of Information Management, 70(5), 481-497.

Thelwall, M. & Mas-Bleda, A. (2018). YouTube science channel video presenters and comments: Female friendly or vestiges of sexism? Aslib Journal of Information Management, 70(1), 28-46.

Thelwall, M. (2018). Can social news websites pay for content and curation? The SteemIt cryptocurrency model. Journal of Information Science, 44(6), 736–751.

Ahmed, W. (2018). Using Social Media Data for Research: An Overview of Tools. Journal of Communication Technology, 1(1), 77-94.

Thelwall, M. (2018). Social media analytics for YouTube comments: Potential and limitations. International Journal of Social Research Methodology, 21(3), 303-316. doi:10.1080/13645579.2017.1381821 [ local copy]

Holmberg, K., & Hellsten, I. (2014). Analyzing the climate change debate on Twitter: content and differences between genders. In Proceedings of the 2014 ACM conference on Web science (pp. 287-288). ACM.

Terlumun, I. T., Appollm, Y. I., Ibrahim, F. J., Mamman, F. S., Yusuf, A. K., & Ibrahim, A. H. (2018). Determining the Effectiveness of YouTube Videos in Teaching and Learning with Mozdeh Algorithm. International Journal of Education and Evaluation.

Thelwall, M., & Cugelman, B. (2017). Monitoring Twitter strategies to discover resonating topics: The case of the UNDP. El profesional de la información.

Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418.

Thelwall, M. (2008). No place for news in social network web sites? Online Information Review, 32(6), 726-744.

Extra resources

• FAQ
• User guide
• Removing spam texts.
• Theoretical overview and introduction to time series and sentiment analysis for Twitter chapter.
• The YouTube Comment Term Frequency Comparison (CTFC) method.
• Not about Mozdeh, but this paper gives useful ideas about interpreting the results: Sen, I., Flöck, F., Weller, K., Weiß, B., & Wagner, C. (2021). A total error framework for digital traces of human behavior on online platforms. Public Opinion Quarterly, 85(S1), 399-422.

Mozdeh Screenshots

Sentiment and retweet/reviews/citation graphs and the top of the word frequency table.

Mozdeh can produce a time series graph of the volume of texts for your topic. The graph below is a section of a screenshot showing the hourly volume of tweeting for a corpus of tweets related to the 2011 UK Riots in 2011.

Mozdeh can produce a time series graph of the percentage of texts matching a query for your topic. The graph below is a section of a screenshot showing the hourly percentage of tweets mentioning police in a UK Riots 2011 corpus.

Mozdeh can produce a time series graph of the average positive and negative sentiment of texts for your whole topic or just the texts matching a query for your topic. The graph below is a section of a screenshot showing the hourly average sentiment of tweets mentioning police in a UK Riots 2011 corpus.

Mozdeh can search for texts matching a query for your topic. The picture below is a section of a screenshot showing some of the results of a search for Wolverhampton in a UK Riots 2011 Twitter corpus.

If you can't use Windows, then you might try COSMOS for Mac/Linux or investigate this list of social media software.