Detecting malicious behavior in social platforms via hybrid knowledge- and data-driven systems

Paredes, Jose N.; Simari, Gerardo I.; Martínez, María Vanina; Falappa, Marcelo Alejandro

doi:10.1016/j.future.2021.06.033

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Furthermore, we make significant advances on [33] by supporting static predicates, and having in-built capabilities for non-graph reasoning, and type checking as detailed in section 2.…”

Section: Methodsmentioning

confidence: 99%

PyReason: Software for Open World Temporal Logic

Aditya¹,

Mukherji²,

Balasubramanian³

et al. 2023

Preprint

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The growing popularity of neuro symbolic reasoning has led to the adoption of various forms of differentiable (i.e., fuzzy) first order logic. We introduce PyReason, a software framework based on generalized annotated logic that both captures the current cohort of differentiable logics and temporal extensions to support inference over finite periods of time with capabilities for open world reasoning. Further, PyReason is implemented to directly support reasoning over graphical structures (e.g., knowledge graphs, social networks, biological networks, etc.), produces fully explainable traces of inference, and includes various practical features such as type checking and a memory-efficient implementation. This paper reviews various extensions of generalized annotated logic integrated into our implementation, our modern, efficient Python-based implementation that conducts exact yet scalable deductive inference, and a suite of experiments. PyReason is available at: github.com/lab-v2/pyreason.

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“…Furthermore, we make significant advances on [33] by supporting static predicates, and having in-built capabilities for non-graph reasoning, and type checking as detailed in section 2.…”

Section: Methodsmentioning

confidence: 99%

PyReason: Software for Open World Temporal Logic

Aditya¹,

Mukherji²,

Balasubramanian³

et al. 2023

Preprint

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show abstract

“…Note that this rule involves a function avg that will be used to forecast the viral state of each post and it returns the required value by computing an average for the values associated with each of the node's reposts. We refer the reader to [48] and [49] for further discussions about diffusion rules.…”

Section: A System For Detecting Hate Speech In Social Platformsmentioning

confidence: 99%

Engineering user-centered explanations to query answers in ontology-driven socio-technical systems

Teze

Paredes

Martínez

et al. 2024

Self Cite

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The role of explanations in intelligent systems has in the last few years entered the spotlight as AI-based solutions appear in an ever-growing set of applications. Though data-driven (or machine learning) techniques are often used as examples of how opaque (also called black box) approaches can lead to problems such as bias and general lack of explainability and interpretability, in reality these features are difficult to tame in general, even for approaches that are based on tools typically considered to be more amenable, like knowledge-based formalisms. In this paper, we continue a line of research and development towards building tools that facilitate the implementation of explainable and interpretable hybrid intelligent socio-technical systems, focusing on features that users can leverage to build explanations to their queries. In particular, we present the implementation of a recently-proposed application framework (and make available its source code) for developing such systems, and explore user-centered mechanisms for building explanations based both on the kinds of explanations required (such as counterfactual, contextual, etc.) and the inputs used for building them (coming from various sources, such as the knowledge base and lower-level data-driven modules). In order to validate our approach, we develop two use cases, one as a running example for detecting hate speech in social platforms and the other as an extension that also contemplates cyberbullying scenarios.

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“…There are real challenges concerning how to exploit OSINT in effective and reliable ways; for example, the nature of social media sources is such that it is often difficult to distinguish between witness information and hearsay. Reliability of sources and reports is an important concern in these settings, CISpaces can be extended to include more complex aggregations of results to mitigate these issues (Ouyang et al, 2016a;2016b) or with automated support in detecting those responsible for propagating misinformation (Paredes et al, 2021). Further, this should not be seen solely as analysts passively consuming open source intelligence, but utilising networks of contributors through crowd-sourced queries.…”

Section: Information Requirements Crowdsourcing and Social Sensingmentioning

confidence: 99%