Why Profanity Analysis Matters in Social Media Monitoring

Social media has provided people with unprecedented freedom of speech. However, with such immense capability also stems the freedom of using profanities.

While in some situations the usage may be harmless, in others such expletives may be intentional and harmful for the audience.

It is to prevent such disrespectful use of swear words and vulgarism that profanity detection is crucial in social media monitoring.

What is Profanity Analysis?

Profanity analysis is the process using which computers detect the use of profane language on social media or other online platforms where users post text.

Profanity is essentially the use of words or phrases to intend notional offense and may demonstrate disrespect, derogation, negativity, etc. towards a group or other individuals. The use of profane language makes the internet space an unpleasant experience for civic users who gather online for genuine purposes.

Profanity analysis helps identify the use of objectionable words and phrases and filters them out so that civic users can have a good experience.

Why Do We Need Profanity Analysis?

There were about 62.3% of active social media users in the world as of January 2024. Suppose your company uses an employee communications tool and runs social media accounts. In that case, it is vital to employ profanity checker tools to promote a pleasant and safe browsing experience for everyone on your channels and networks.

For businesses in healthcare, finance, or education, keeping your online spaces clean is extra important. This means watching out for bad language (swearing) on your websites, social media, and anywhere else you interact with people online.

There are general rules about what you can and can't say online, and these businesses need to be extra careful to follow them. These rules cover things like spreading lies about people (defamation), being mean to others (harassment), and promoting hatred (hate speech).

You can train and automate AI models to check for profanity on all the social channels of a business and conduct sentiment analysis for a detailed breakdown of communications. NLP models can understand the exchanges between two persons on a brand's social channels, helping flag objectionable content, and remove it from the channels before it can cause damage to a brand's reputation.

Why Profanity Analysis Matters In Social Media Monitoring?

Six key reasons make profanity analysis crucial for businesses in social media monitoring:

1. Brand Reputation Management

Profanity can lead to negative brand perceptions and loss of trust among customers. It can directly impact sales. To address this, brands can leverage profanity checkers on social media monitoring to identify, flag, and remove offensive, hurtful, or sensitive content. It helps with preserving brand reputation and credibility in the market.

For example, Flipkart has a customer review filter that flags comments that violate its feedback policy:

2. User Experience and Engagement

Profanity can directly create customer disengagement on brand channels by creating discomfort and offensive environments. Brands need to moderate each comment that is posted using automation capabilities of AI-based profanity checker tools to promote a healthy and engaging conversation online.

For example, Fuk.ai is an AI-based profanity checker that detects hate speech and profanity and auto-deletes inappropriate content from websites, apps, and platforms. 

3. Community Guidelines Adherence

Online community guidelines mandate that all participants should use respectful language and avoid offensive messages toward the brand or other participants. Businesses can integrate community guidelines and policies with profanity checker tools to maintain a healthy atmosphere online.

They can also be programmed to automatically suspend accounts that repeatedly violate these guidelines.

4. Sentiment Analysis Enhancement

Profanity is often accompanied by the expression of strong emotion, which can easily be detected using sentiment analysis.

Businesses should integrate profanity analysis and sentiment analysis into their social media monitoring algorithms to better understand customer emotions based on the context and tone of the text, and act accordingly.

5. Audience Targeting and Personalization

Profanity analysis is crucial for businesses to understand the overall consumer sentiment towards their products and services.

It also helps with understanding the demographic behavior and preferences to give context to the use of foul words. It helps with creating personalized content that resonates better with specific demographics.

For example, if a restaurant sees a rise in reviews slamming their service with words like "crap" and "terrible," they might target future email blasts with apologies and special offers to win back those angry customers.

6. Crisis Management

It is possible to identify potential crises using profanity checker tools. Brands should watch out for sudden spikes in offensive language as they could point to a problematic service or product. It could help with early mitigation and averting a brand crisis, with the negative impact of issues left ignored.

For example, news media houses may use profanity analysis to identify potential public outrage generated by a piece of specific news and act early to neutralize or appease the strong sentiment.

What Is The Role Of AI In Profanity Analysis?

The use of AI has helped businesses incorporate modern methods of profanity analysis deeply into their sentiment analysis techniques. By detecting profane language and accounting for the negativity it creates, sentiment analysis models can easily promote healthy conversations while removing offensive content from brand channels.

For example, when a brand monitors product reviews posted on its social media channels, it is important to distinguish between a comment that genuinely highlights the concerns of a customer and a comment that uses profane language to do the same.

The sentiment in both comments is negative, but sentiment analysis coupled with profanity checking will help the brand retain the genuine review while flagging the offensive comment.

Practical use cases

Profanity analysis is widely used by businesses across their social media channels to promote pleasant conversations online:

1. Social Media Moderation

Profanity analysis filters are often used to categorize profanity used in user-generated content so that the content follows the guidelines of the platform. It is possible to automate these tools to remove offensive content or mask it so that a positive browsing experience is maintained on all brand channels.

For example, the popular game Minecraft employs Java Edition Realms profanity filters. It has the power to suspend customer accounts if they do not conform to the guidelines.

2. Customer Support Chatbots

Customer service is an extremely sensitive matter. When a dissatisfied customer uses chatbots to find resolutions to their issues, they are already in a bad mood. They are highly likely to start using profanities if the chatbot is unable to address the issue. Chatbots can be programmed with AI-based profanity checker tools that remove offensive words, and respond in a professional manner to placate the angry customer.

eCommerce platforms like Amazon employ profanity filters in their chatbots that either mask or remove offensive language from the interaction.

3. Content Filtering

On public forums and blogs, profanity filters work by flagging content that contains offensive language. The user is sent a warning and if they persist in using bad language in their posts, their account may be suspended.

It is important to ensure that public forums foster positivity in conversations. This enables the audiences to have pleasant experiences with the forum and its content.

Conclusion

Profanity analysis is a crucial aspect of enabling a safe online experience for audiences. Using machine learning, NLP, and deep learning models, it is possible to employ robust profanity detection methods in conjunction with sentiment analysis.

Even with challenges like cultural nuances and contextual errors, it is still possible to improve on the existing technologies through exhaustive data preprocessing and preparation. Some of the emerging technologies that can enable more efficient profanity detection are already making a difference – like multimodal profanity detection that applies to all forms of input data, whether text, images, videos, or audio.

For businesses looking to deploy effective profanity analysis, MarkovML provides a reliable framework for Responsible AI. Businesses can build responsible AI solutions that are trustworthy and transparent, well suited for profanity detection.

The platform offers features such as LLM assessment, enhanced interoperability, regulatory compliance, and much more. To know more about how you can develop a custom profanity analysis tool, visit Markov.