12,715 research outputs found
Characterizing Pedophile Conversations on the Internet using Online Grooming
Cyber-crime targeting children such as online pedophile activity are a major
and a growing concern to society. A deep understanding of predatory chat
conversations on the Internet has implications in designing effective solutions
to automatically identify malicious conversations from regular conversations.
We believe that a deeper understanding of the pedophile conversation can result
in more sophisticated and robust surveillance systems than majority of the
current systems relying only on shallow processing such as simple word-counting
or key-word spotting.
In this paper, we study pedophile conversations from the perspective of
online grooming theory and perform a series of linguistic-based empirical
analysis on several pedophile chat conversations to gain useful insights and
patterns. We manually annotated 75 pedophile chat conversations with six stages
of online grooming and test several hypothesis on it. The results of our
experiments reveal that relationship forming is the most dominant online
grooming stage in contrast to the sexual stage. We use a widely used
word-counting program (LIWC) to create psycho-linguistic profiles for each of
the six online grooming stages to discover interesting textual patterns useful
to improve our understanding of the online pedophile phenomenon. Furthermore,
we present empirical results that throw light on various aspects of a pedophile
conversation such as probability of state transitions from one stage to
another, distribution of a pedophile chat conversation across various online
grooming stages and correlations between pre-defined word categories and online
grooming stages
Detecting and Explaining Crisis
Individuals on social media may reveal themselves to be in various states of
crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis
from social media text automatically and accurately can have profound
consequences. However, detecting a general state of crisis without explaining
why has limited applications. An explanation in this context is a coherent,
concise subset of the text that rationalizes the crisis detection. We explore
several methods to detect and explain crisis using a combination of neural and
non-neural techniques. We evaluate these techniques on a unique data set
obtained from Koko, an anonymous emotional support network available through
various messaging applications. We annotate a small subset of the samples
labeled with crisis with corresponding explanations. Our best technique
significantly outperforms the baseline for detection and explanation.Comment: Accepted at CLPsych, ACL workshop. 8 pages, 5 figure
A systematic survey of online data mining technology intended for law enforcement
As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in
User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17
A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research
Purpose:
Computational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analyse manually. This article aims to give an overview of current work applying text mining methodologies in the study of IPV, as a starting point for researchers wanting to use such methods in their own work.
Methods
This article reports the results of a systematic review of academic research using computational text mining to research IPV. A review protocol was developed according to PRISMA guidelines, and a literature search of 8 databases was conducted, identifying 22 unique studies that were included in the review.
Results:
The included studies cover a wide range of methodologies and outcomes. Supervised and unsupervised approaches are represented, including rule-based classification (n = 3), traditional Machine Learning (n = 8), Deep Learning (n = 6) and topic modelling (n = 4) methods. Datasets are mostly sourced from social media (n = 15), with other data being sourced from police forces (n = 3), health or social care providers (n = 3), or litigation texts (n = 1). Evaluation methods mostly used a held-out, labelled test set, or k-fold Cross Validation, with Accuracy and F1 metrics reported. Only a few studies commented on the ethics of computational IPV research.
Conclusions:
Text mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches
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