Description:FIELD OF THE INVENTION
This invention relates to a system and method for cross-dimensional detection of suicidal ideation in contextual social text streams using natural language processing.
BACKGROUND OF THE INVENTION
Mental health conditions, primarily suicidal thoughts and behaviors, are increasingly recognized as a serious global issue. The emergence of digital communication technology means that many people express feelings of emotional distress and suicidal thoughts using social media. These expressions of distress are often subtle, context-dependent, blended with everyday conversations and are thus difficult to identify using traditional monitoring methods.
Current approaches to identifying suicidal intent using social media data describe manual monitoring, common keyword searches, and rule-based systems, of which many produce excessive false positives. Many systems lack the capabilities to understand the deeper meaning or emotional context of a user's message. Another limitation for the use of social media accounts is that most approaches are designed to identify data from one language and one platform, without taking into account the rapidly evolving and expanding multi-platform, multi-lingual world of social media.There is an urgent need for a scalable, intelligent system that can automatically and convert user-generated unstructured text, posted in social media, into identified suicidal ideation - in a variety of languages, and across different platforms and language styles, by leveraging modern Natural Language Processing methods. The purpose of this, is to help the mental health community in early identification and intervention to potentially save lives.
he existing commercial products that target suicidal ideation detection using online content primarily depend on a mix of manual monitoring, sentiment analysis, and user reports. Both Ginger.io and Woebot offer mental health assistance through tracking behavioral data and AI-driven conversations. These sites seek to identify indicators of distress but depend on active user participation and are usually confined to a particular platform or language, limiting their capacity to automatically detect passive indicators of suicidal ideation in a broader scope of online content. **Crisis Text Line** provides crisis counseling via text messaging, offering real-time crisis intervention but does not have the capacity to automatically monitor content across multiple social media platforms.
Furthermore, Facebook's Suicide Prevention Tools apply AI and keyword identification to identify content that is potentially suicidal. Yet, these tools produce false positives and do not identify indirect or context-based suicidal ideation since they predominantly look for particular words or outright articulations of harm. Sentiment analysis software such as IBM Watson can examine text for emotional indicators but are typically not trained on identifying subtle emotional distress or suicidal ideation, and therefore miss important contextual information. Current products thus depend on identifying negative feelings or injurious content, but they do not capture the intricate, indirect, and contextual language of suicidal ideation that does not always need to be spelled out.

Current commercial practice is largely comprised of a combination of passive detection by keyword filters and active user interaction, typically depending on a single platform or language. This leaves gaps in detection, scalability, and emotional intelligence, pointing to the necessity for a more holistic, real-time, and context-considerate solution that can work across various platforms, languages, and emotional levels.
The current solutions that are available are lacking in completely addressing the issue of detecting suicidal ideation in online material in some important respects. For one, most current systems depend on keyword detection or sentiment analysis, which do not comprehend the underlying emotional content of user messages. Suicidal ideation is usually expressed indirectly or subtly, and users may discuss feelings of isolation or hopelessness without explicitly referencing suicide. Such systems thus miss such subtleties of expression and hence miss false negatives and even possibly miss people who are at risk. Further, keyword-based systems are generally prone to high false positives, marking material which is not harmful, so a sentence such as "I'm done" may be an expression of frustration in a non-suicidal one but will still be marked, resulting in undue interventions or misidentifications.
Another limitation is the platform and language constraints of many current solutions. For example, Facebook's suicide prevention tools focus on content within their platform and are limited to specific languages. This diminishes the effectiveness of these systems in the global, multi-platform world, where users connect across social media platforms and communicate in multiple languages. Most solutions also depend on active user participation, as in the case of Ginger.io and Woebot.. These solutions are only viable if people take an active role in seeking help, which neglects those who may not realize they are experiencing distress or who are afraid to seek help. Furthermore, current systems typically concentrate only on textual content without taking into account additional pertinent data, e.g., user behavioral patterns or interactions in various online communities.
This limited perspective makes them ineffective in detecting early warning signs of distress. Finally, scalability continues to be a major challenge, as the solutions find it difficult to process voluminous amounts of unstructured data over diverse platforms in real-time. Manual surveillance is time-consuming, and conventional algorithms are not adept at processing high volumes of information in time for effective intervention. Overall, the current solutions are not able to completely identify the sophistication and background of suicidal ideation in terms of using limited sources of data and being non-scalable. The limitations point to the necessity of a more elaborate, automated, and context-oriented system that is capable of precisely identifying suicidal ideation on different media and languages and providing support and intervention on time.
The solution to be proposed has several important benefits over conventional approaches. Conventional systems depend mostly on rule-based or keyword-based systems, which tend to be too clumsy in detecting the context-dependent, subtleties of suicidal ideation language and tend to produce high false positives or low detection rates. Our invention, however, uses deep learning-based NLP, sentiment analysis, and emotion detection to detect the actual intent of user-generated content with far greater precision. Also, most up-to-date solutions are confined to one language or platform, but our system supports multiple languages and consolidates information from several social media sites in real time. In contrast to static systems which need to be updated manually, our machine learning models change and evolve automatically using supervised learning. In addition, the presence of an alert mechanism in real-time and a separate dashboard for mental health professionals facilitates timely interventions, something that the usual systems fail to provide.
US12057112: The present disclosure describes a system to use conversation data of patients to detect dangerous mental or physical conditions, such as suicidal thoughts, physical abuse, recent falls, and viral infection. A machine learning system may be trained to identify a dangerous mental or physical condition from conversations based on examples of patients evaluated to have a specific mental or physical condition. Conversations of patients may be monitored, natural language understanding (NLU) processing performed, and a machine learning system used to detect dangerous mental or physical conditions.
US11594012B2: A computer readable medium for analyzing images according to specific kinds of features oriented towards detecting subtle branding features (intentional or otherwise) rather than relying on the usual image similarity detection methods. Also disclosed are steps to enhance standard machine learning techniques to identify new types of transformations by partitioning data into measure-countermeasure windows, which may be either/both detected computationally or inputted into a knowledgebase. The invention further incorporates direct and indirect traits of images that were likely to have been promulgated by a particular group or actor of interest, especially those traits that prove to be more invariant over time (including the use of transformations) which have proven to be more resistant to countermeasures applied in different jurisdictions. More generally, almost all embodiments allow individual feature calculations to be toggled on and off, and to define sets of features according to jurisdiction.
The primary objective of the invention is to provide a scalable and intelligent system for early detection of suicidal ideation in contextual social text streams across multiple platforms and languages.
Another objective is to develop a multi-dimensional analysis approach that captures sentiment, emotion, and contextual meaning beyond simple keyword detection, thereby reducing false positives and false negatives.
A further objective is to integrate machine learning models that learn from large datasets of suicidal ideation to adaptively recognize evolving patterns of distress in online conversations.
The invention also aims to support multilingual and multi-platform content analysis, ensuring applicability in the diverse and globalized environment of social media communication.
Finally, the invention seeks to enable real-time monitoring and alerts that provide mental health professionals with timely insights and actionable information for intervention, potentially saving lives.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention proposed seeks to solve the increasing problem of suicidal ideation detection on social media platforms through the use of sophisticated Natural Language Processing (NLP) methods, machine learning, and multi-platform integration. It offers a holistic solution that can effectively detect suicidal thoughts and behaviors hidden within user-generated content, enabling mental health professionals to intervene early.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invention proposed seeks to solve the increasing problem of suicidal ideation detection on social media platforms through the use of sophisticated Natural Language Processing (NLP) methods, machine learning, and multi-platform integration. It offers a holistic solution that can effectively detect suicidal thoughts and behaviors hidden within user-generated content, enabling mental health professionals to intervene early.
The present invention relates to a system and method for the detection of suicidal ideation in contextual social media text streams using advanced natural language processing (NLP) and machine learning techniques. With the growing use of digital platforms, individuals increasingly express distress and suicidal thoughts online, often in indirect or context-dependent ways. Traditional systems that rely on keyword filtering or manual monitoring fail to capture these subtleties and frequently produce excessive false positives.
The present system introduces a cross-dimensional detection approach that incorporates multiple layers of analysis, including sentiment detection, emotion recognition, and contextual interpretation. This ensures that subtle indicators of distress, such as hopelessness or isolation, are identified even when explicit suicidal terms are absent. The system also addresses the limitations of platform-specific and monolingual solutions by supporting multiple languages and integrating data streams from several social media platforms.
The system is comprised of a data collection module, which interfaces with APIs of various social media platforms to gather real-time user-generated content. A preprocessing pipeline then cleans and structures the unstructured data for further analysis. The processed data is passed into an NLP engine that applies deep learning-based sentiment analysis, named entity recognition, and context-aware emotion detection.
A machine learning classifier, trained on labeled datasets of suicidal ideation, predicts the likelihood of suicidal intent in the analyzed text. When high-risk content is detected, a real-time alert module communicates notifications to mental health professionals via dashboards or mobile applications. This enables proactive intervention and crisis prevention.
The invention provides a scalable, accurate, and adaptive solution that can be deployed globally, offering significant improvement over existing systems in both precision and practical application.
Best Method of Working the Invention
The best method of implementing the invention involves deploying it as a cloud-based software system with modular components for data collection, preprocessing, NLP analysis, classification, and alerting. The data collection module retrieves posts, comments, and messages from multiple social media platforms through authorized APIs. The preprocessing pipeline prepares raw text by performing tokenization, stemming, lemmatization, and removal of irrelevant data.
The NLP engine applies deep learning-based sentiment and emotion analysis along with contextual interpretation to capture the deeper meaning of user-generated content. The processed features are input into a supervised machine learning classifier, trained on large annotated datasets of suicidal ideation, which continuously updates through incremental learning to capture evolving language patterns.
The alert module provides notifications with risk categorization (low, moderate, high) and offers dashboards for mental health professionals to monitor and act. The system is scalable to handle millions of posts per day and supports multiple languages, making it practical for global deployment.
This invention addresses the challenge of identifying suicidal thoughts in online posts, a sophisticated and subtle undertaking that is difficult to manage using conventional monitoring systems. Such systems normally use keyword searches or rule-based approaches, with high false positives being a common outcome or missing subtle expressions of distress. Our invention surpasses mere keyword matching and rather employs deep learning-based NLP methods, such as sentiment analysis, emotion detection, and context-based text interpretation, to pinpoint at-risk individuals with higher accuracy.
Data Gathering and Preprocessing: The process gathers unstructured user-generated textual data from numerous social media sources (e.g., Twitter, Facebook, Instagram, X). Status updates, comments, and other text interactions are included. Text is cleaned through preprocessing techniques such as removal of stopwords, stemming, and tokenization prior to analysis.
Multi-Language and Multi-Platform Integration: One of the most important aspects of this invention is that it can process content in multiple languages, thereby it can be used for global social media data. This makes the system not exclusively using one language or one social media platform. The system processes multiple APIs of different platforms to collect data from different sources and analyze them at the same time.
Advanced NLP Algorithms: Employing higher-level NLP methods such as sentiment analysis, named entity recognition (NER), and emotion detection, the system can perceive the emotional context of the text. It examines the sentiment expressed (positive, negative, or neutral) and detects subtle indicators of distress or depression by contextual interpretation. This is an important step toward identifying those whose suicidal tendency is expressed indirectly.
Machine Learning Pattern Recognition: The system uses machine learning algorithms to identify patterns in the data, learning from labeled datasets of suicidal ideation. These algorithms can improve incrementally through supervised learning, enabling the system to adapt to changing expressions of distress over time. By detecting certain patterns in language (e.g., repeated words or sentence forms associated with emotional distress), the system can more accurately predict possible suicidal ideation.
Real-time Monitoring and Alerts: When an at-risk message is identified, the system sends real-time alerts to mental health professionals or appropriate support systems. This early warning enables timely intervention, such as calling the person with support resources or contacting emergency services as needed.
Scalability and Flexibility: The innovation is conceived to scale alongside the increasing amounts of user content across various sites. It handles millions of messages every day and processes data in real time without losing accuracy. Furthermore, the system can also be tailored to concentrate on special groups of users or communities, such as teens, or watch particular types of content, like discussions related to mental health.
Implementation: The envisioned invention would be deployed as a cloud-based software with the following modules:
● Data Collection Module: Interfaces with APIs of major social media sites to gather data in real time.
● Text Preprocessing Pipeline: Cleans and preprocesses raw text data for analysis.
● NLP and Machine Learning Model: Analyzes the preprocessed data to identify suicidal ideation and forecast risk.
● Real-time Alert System: Alerts stakeholders such as mental health professionals when high-risk content is identified.
● User Interface: Offers intuitive dashboards and reporting tools for experts to track and act upon detected risks.
This invention uniquely combines deep learning-based NLP, emotion detection, and real-time multi-platform integration to accurately detect suicidal ideation in multilingual social media content, enabling early intervention with minimal false positives.
# Load a pre-trained sentiment-analysis pipeline
sentiment_analyzer = pipeline("sentiment-analysis")
# Example user-generated text
texts = [
"I don't want to live anymore...",
"I'm just tired of everything.",
"Today was a good day!",
"Sometimes I feel like I'm invisible."
]
# Analyze each text
for text in texts:
result = sentiment_analyzer(text)[0]
label = result['label']
score = result['score']
# Simple risk check
if label == 'NEGATIVE' and score > 0.90:
risk_status = " High Risk of Suicidal Ideation"
elif label == 'NEGATIVE':
risk_status = "Moderate Risk"
else:
risk_status = "Low Risk"
print(f"Text: {text}\nSentiment: {label} ({score:.2f})\nRisk: {risk_status}\n")
Advantages of the Invention
The proposed invention offers a significant advantage over traditional keyword-based or rule-driven systems by incorporating deep learning-based NLP and emotion recognition, which enables it to capture subtle, indirect, and context-dependent expressions of suicidal ideation. Unlike conventional methods that often flag harmless phrases as risky, this system reduces false positives and ensures that interventions are directed toward genuinely at-risk individuals.
Another advantage lies in the system’s multi-platform and multilingual capability, which makes it adaptable to the global digital ecosystem. Unlike existing solutions restricted to a single platform or language, the invention can process diverse content streams from multiple social media channels in real time and across different languages, greatly expanding its applicability and relevance.
The use of machine learning with incremental adaptation further enhances the system’s effectiveness. By learning continuously from new data, the model evolves with changing linguistic patterns and cultural expressions of distress, ensuring that it remains accurate and up to date without requiring constant manual retraining.
Additionally, the invention provides real-time monitoring and alerting features, enabling immediate notification to mental health professionals when high-risk content is detected. This proactive approach facilitates timely intervention, potentially preventing self-harm or suicide. The presence of an intuitive dashboard for professionals ensures that the insights generated are actionable and accessible.
Finally, the invention is scalable and robust, designed to process massive volumes of unstructured social media data efficiently. This scalability ensures that it can be deployed in large populations, institutions, or nationwide mental health programs, making it a powerful tool for both clinical use and public health initiatives.
, Claims:1. A system for cross-dimensional detection of suicidal ideation in contextual social text streams, comprising a data collection module configured to gather user-generated textual content from multiple social media platforms, a preprocessing pipeline adapted to clean and normalize text, a natural language processing unit for sentiment, emotion, and context detection, a machine learning model trained on suicidal ideation datasets to identify risk patterns, and a real-time alert module to notify stakeholders when high-risk content is detected.
2. The system as claimed in claim 1, wherein the data collection module interfaces with APIs of multiple platforms to retrieve user posts, comments, and messages.
3. The system as claimed in claim 1, wherein the preprocessing pipeline performs tokenization, stemming, lemmatization, and stop-word removal for improved analysis.
4. The system as claimed in claim 1, wherein the NLP unit applies sentiment analysis, emotion recognition, and named entity recognition to extract contextual meaning from text.
5. The system as claimed in claim 1, wherein the machine learning model employs supervised learning techniques using labeled datasets of suicidal ideation expressions.
6. The system as claimed in claim 1, wherein the alert module transmits notifications to mental health professionals through a dashboard or mobile application interface.
7.The system as claimed in claim 1, wherein the system supports multilingual analysis of social media content across different platforms.
8. The system as claimed in claim 1, wherein the machine learning model adapts dynamically using incremental learning to capture evolving suicidal ideation expressions.
9. The system as claimed in claim 1, wherein the alert module provides risk classification levels including low, moderate, and high-risk categories.
10. The system as claimed in claim 1, wherein the system is deployed on a cloud-based infrastructure to ensure scalability and real-time performance.