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This probabilistic approach allowed researchers to assess how toxic content was and in which way it was toxic, enabling distinctions to be made between Salafi actors online voicing conservative expressions of religion, and those voicing supremacist, sectarian and violent positions. It also gave subject matter experts control over input into the model and the data it was trained on. In addition to assigning toxicity scores to single pieces of content, it is possible to aggregate the average level of toxicity per platform, language and over time through such an approach.
This allows researchers to answer important questions, such as which platforms are most and least affected by violent and discriminatory Salafi content; what events lead to spikes in dehumanising and supremacist rhetoric; and what differences can be identified between different linguistic Salafi communities.
There are several further advantages to applying toxicity analysis for the analysis of Salafi communication online. Foremost, it allows for the fast analysis of large datasets, and every step and decision taken during the analysis is explainable and replicable.
Through building on existing lexicons used during previous research, it can also enable increasingly fine-tuned results and granular insights. In a recent paper , our partners at Textgain describe the use of their AI system for the detection of antisemitism, evidencing how similar approaches could be used to analyse many different types of toxic content from extremist discourse across the ideological spectrum, as well as racist, antisemitic, anti-Muslim, misogynist or anti-LGBT content.
Based on these considerations, we propose that toxicity is a helpful concept to consider when thinking about the potential harmful expressions within online Salafi communities. In going beyond a narrow focus on violent radicalisation, it allows researchers to make crucial distinctions between Salafi content that advocates for different types of toxic positions, and content that merely expresses conservative guidance on religious and social issues. It is therefore a suitable approach for future research that aims to track problematic manifestations of Salafism online without reducing the entire movement to its violent fringe.
Sylvain's conspiratorial thinking made him wary of government and society, and led him to sell his home and move to higher lands in fear the end of the world would come because of a prophecised date.
ISD US analyzes the key trends in election denialist behaviour during the midterms, and what to expect going forward. Toxicity Analysis and Salafism Salafism is a reformist branch of Sunni Islam that aspires to return to the faith practised by the prophet Mohammed and his earliest followers. Toxicity Detection Over the course of a year-long research project looking at the intersections of Gen-Z identities and Salafism in the digital space, ISD researchers applied a toxicity-based approach to analyse a dataset of 3.
Extreme Dialogue: Former conspiracist shares story to help others understand the realities of extremism Sylvain's conspiratorial thinking made him wary of government and society, and led him to sell his home and move to higher lands in fear the end of the world would come because of a prophecised date. Five key takeaways from election denialist activity during the midterms ISD US analyzes the key trends in election denialist behaviour during the midterms, and what to expect going forward.
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The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes. Manage options Manage services Manage vendors Read more about these purposes. Accept Functional cookies Preferences Save preferences Preferences. Manage consent. These systems mainly use rules-based workflows alongside large libraries to identify and alert users to pre-defined issues within documents. These systems can be cumbersome and complex, demanding constant updates and manual intervention to guarantee any measure of success.
Additionally, these systems often fail when multiple languages and regional variations in law are applied. However, the need for a solution to the problem of document analysis and semantic recognition continues to grow.
Document research and management systems are now using strategies based on machine learning to classify, filter and extract context-based content. These systems help users identify relevant structured portions of legal text semi-automatically such as contract clauses. However, while knowledge management systems can deliver automated detection of matching text sections, they often fail to identify meaning or the change of purpose.
The application of artificial intelligence AI to the problem of identifying and comparing semantic meaning has, at last, started to gain ground. Companies such as ThingsTHINKING from Karlsruhe have developed technologies over the previous 14 years that can already identify and compare semantic meanings within complex legal documentation.
It can be applied to many use cases such as CV matching, NDA analysis, contract review, contract renewals, international contract translation and more. The Semantha interface allows users to upload multiple documents such as contracts to an interface for analysis.
These documents can be in different formats Word or PDF and various languages. The user can then run a comparison or search against the records to find and identify matching sections of text that imply similar or the same meaning.
Semantha instantly identified clauses within each constitution that matched the same meaning irrespective of language. The Semantha platform can easily compare the matching phrases for similarity matches with adjustable thresholds.
It makes these comparisons and identifies matches in semantic meaning despite different wording and terms in each document. Perhaps just as importantly, the Semantha platform quickly identifies if a close match is not available between documents helping users to discover if an expected intention in meaning is missing from within a document.
Although the Semantha platform can quickly identify the presence of an expected item of text with specific meaning, this is not enough for most organisations. For example, a clause specified within a contract may stipulate a penalty.
Our client might believe that this penalty is unacceptable and we need the AI to help us identify and highlight the inclusion of this unacceptable condition. Working with automation tools such as UiPath, users can integrate the Semantha platform to their document libraries such as SharePoint and any gateways where documents are arriving at the organisation such as email.
Semantha can automatically scan incoming documents looking for semantic meanings that it knows to highlight triggering an alert and actions that the user can easily manage. Users can decide on the level of integration they want within their document management workflows and they can maintain human intervention where specialists need to evaluate semantic changes in contracts.
As they become more confident in the accuracy of the Semantha platform, they can rely increasingly on automation and AI to deliver accurate results far more quickly. For anyone dealing with many legal documents such as contract renewals, having a system like Semantha onboard could save the organisation many hundreds of hours in analysis.
Channel Partnerships , working in partnership with Tech Data is here to help organisations evaluate the technology. Channel Partnerships can set up a meaningful and tailored demonstration that will show the Semantha platform working to provide you a real-world evaluation. Channel Tools has now added the range of Teltonika Networks devices, capabilities and platforms to .
Read More. Channel Tools has now added the range of Milesight IoT devices, capabilities and platforms to the . News and views. There are many benefits partnering with us. Simply complete the form at the bottom of this page to discuss how the program can sky-rocket your visibility in any technology market place.
We have huge experience in the creation of new channels and we can create introductions, leads and business opportunities for your sales teams. Understanding nuanced meaning in legal documents How AI is revolutionising the granular analysis and comparison of meaning within contracts and complex documents.
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