Text Moderation: Methods, Techniques and Problems

Text moderation is the process of reviewing and controlling text content to ensure that it complies with certain rules, standards or criteria. This process is often used in online environments such as forums, social networks, blogs and other web resources to prevent unwanted or inadmissible content from being posted.

Text moderation can include automated methods, such as filters based on keywords or phrases, as well as manual review by moderators. The goal of text moderation ranges from preventing the spread of spam and hate speech to complying with the laws and rules of a particular web resource.

The moderation process helps create safer and more acceptable online communities by keeping the quality of content and its compliance with established standards at the required level.

Text moderation can be carried out using various methods and techniques, depending on the specific requirements and goals of the platform. Here are some common methods and techniques of text moderation:

1. Automated filters:

   • Using software algorithms to automatically detect and block texts that contain prohibited content or keywords.

2. Community moderation:

   • Involves community members in the moderation process. Users can flag or report unwanted content, which helps to identify problems.

3. Manual moderation (pre-moderation and post-moderation):

   • Employing real moderators who analyze texts and decide whether to block or approve content in accordance with the rules and standards of the platform.

4. Using machine learning:

   • Developing systems based on artificial intelligence that can automatically learn to recognize and filter unwanted content.

5. Word filters and dictionaries:

   • Filtering texts using pre-compiled dictionaries of prohibited words or phrases.

6. Sentiment analysis:

   • Using algorithms to determine the emotional tone of a text. This can help identify content that contains hate speech or threats.

7. Retrospective moderation:

   • Proactive analysis of content after it is published in order to detect and remove violating content.

8. Real-time moderation:

   • Instant analysis and blocking of content before it appears on the platform.

9. Trigger word technique:

   • Using certain words or phrases as triggers to activate moderation actions.

10. Moderator training:

    • Continuous training of moderators for more effective and accurate detection of violations and compliance with standards.

11. Naive classifier:

    Using a dictionary of obscene vocabulary to simply determine whether "bad" words are present in a text. This method is fairly simple and fast, but it may miss some variants of unwanted content that do not use explicit obscene words.

12. Logistic regression:

    Applying logistic regression using TF-IDF (term frequency-inverse document frequency) and the TweetTokenizer from the nltk library to create text vectors. This method makes it possible to estimate the probability that a text is toxic using the logistic function. The advantage of TF-IDF is that it takes into account the importance of words in the context of the document and of the corpus as a whole.

13. BERT (Bidirectional Encoder Representations from Transformers):

    BERT involves using a pre-trained deep learning model to analyze text and determine its toxicity. BERT provides a better understanding of the context and semantics of a text, which makes it more effective at processing complex language structures and meanings.
    This architecture, which combines simple methods (a dictionary of obscene vocabulary, logistic regression) with more complex ones (BERT), creates a more reliable text moderation system. Such an approach allows a more flexible response to various forms of unwanted content, including content that avoids explicit obscene words.

Effective text moderation usually involves a combination of these methods to achieve the best results.

Problems of text moderation

Despite its importance, text moderation faces a number of problems and challenges:

1. False positives:

   • Automated filtering systems may incorrectly identify some content as prohibited (false positives), which leads to the blocking of legitimate texts.

2. False negatives:

   • Systems may miss unwanted content (false negatives), which reduces the effectiveness of moderation.

3. Adaptability of violators:

   • Malicious actors constantly develop new ways to bypass filters and moderation systems, making violations harder to detect.

     This means that malicious actors who post content that violates platform rules are constantly looking for new ways to bypass filters and moderation systems. This creates a permanent rivalry between the developers of moderation systems and those who seek to circumvent them.

     Some methods that malicious actors may use to bypass moderation include:

   • Altered spelling: Using altered or nonstandard spellings of words (leet speak, Hexspeak, the "Padonki jargon", Pig Latin
     Jargon File, alternative symbols, Verlan, text contamination, cryptophasia, the "blue language", "salty language" or "sun language" (less often the "brick" or "sausage" language), "white language", "green language", "secret language", a language similar to Pig Latin called the "toad foreign language" (zapo-erdara), the "goat foreign language" (ahuntz-erdara), the "witches' language" (sorgin-solasa or belagile-solasa), the "toad language" (zapo-hizkuntza) or the "toad Basque language" (zapo-euskara), jargon, argot, slang, etc.).

   • Embedding symbols and codes: Inserting obscene or prohibited words using Unicode characters, codes or other methods.

   • Special characters and spaces: Using special characters or spaces to mask prohibited content.

   • Deceiving keyword filters: Hiding prohibited words by using different wordings or synonyms.

   • Changing the format: Converting text into an image or another format to bypass text filters.

   • Switching languages: Switching to other languages to bypass language filters.

   • These methods require moderation systems to be constantly updated and adapted in order to effectively detect new ways of evasion. The developers of moderation systems must be prepared for the fact that malicious actors will constantly find innovative methods of bypassing them, and regular updates and improvements become an integral part of combating this problem.

   • using AI to hide the distinctive features of unwanted content

   • using images

   • using unfinished thoughts whose result is a prohibited word, phrase or idea

4. Contextual problems:

   • Some statements may be understood differently in different contexts, which makes it harder to determine the author's intentions.

5. Difficulties in multilingual environments:

   • Processing texts in several languages can be difficult because of language specifics and cultural differences.

6. Neutrality of moderation:

   • Moderators may make subjective judgments, which can lead to uneven moderation depending on their own beliefs and points of view.

7. Privacy protection:

   • Moderation requires access to personal data, which raises questions about privacy and security.

8. Inconsistency of assessments:

   • Different moderators may give different assessments of the same content, which creates the potential for inconsistent moderation.

9. Preserving freedom of speech:

   • It can be difficult to balance maintaining a safe online space with respecting freedom of speech.

Solving these problems requires continuous improvement of technologies, training of moderators, and ensuring transparency and accountability in the moderation process.

See also

• [[b4749]]
• [[b11845]]
• [[b4379]]

See also

• Bayesian Spam Filtering
• Phishing — an attempt to obtain confidential information
• Rules for mass mailing or how not to get into spam? PHP OpenDkim + Postfix setting