1,275 research outputs found
Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities
One of the major hurdles preventing the full exploitation of information from
online communities is the widespread concern regarding the quality and
credibility of user-contributed content. Prior works in this domain operate on
a static snapshot of the community, making strong assumptions about the
structure of the data (e.g., relational tables), or consider only shallow
features for text classification.
To address the above limitations, we propose probabilistic graphical models
that can leverage the joint interplay between multiple factors in online
communities --- like user interactions, community dynamics, and textual content
--- to automatically assess the credibility of user-contributed online content,
and the expertise of users and their evolution with user-interpretable
explanation. To this end, we devise new models based on Conditional Random
Fields for different settings like incorporating partial expert knowledge for
semi-supervised learning, and handling discrete labels as well as numeric
ratings for fine-grained analysis. This enables applications such as extracting
reliable side-effects of drugs from user-contributed posts in healthforums, and
identifying credible content in news communities.
Online communities are dynamic, as users join and leave, adapt to evolving
trends, and mature over time. To capture this dynamics, we propose generative
models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian
Motion to trace the continuous evolution of user expertise and their language
model over time. This allows us to identify expert users and credible content
jointly over time, improving state-of-the-art recommender systems by explicitly
considering the maturity of users. This also enables applications such as
identifying helpful product reviews, and detecting fake and anomalous reviews
with limited information.Comment: PhD thesis, Mar 201
Information-seeking on the Web with Trusted Social Networks - from Theory to Systems
This research investigates how synergies between the Web and social networks can enhance the process of obtaining relevant and trustworthy information. A review of literature on personalised search, social search, recommender systems, social networks and trust propagation reveals limitations of existing technology in areas such as relevance, collaboration, task-adaptivity and trust.
In response to these limitations I present a Web-based approach to information-seeking using social networks. This approach takes a source-centric perspective on the information-seeking process, aiming to identify trustworthy sources of relevant information from within the user's social network.
An empirical study of source-selection decisions in information- and recommendation-seeking identified five factors that influence the choice of source, and its perceived trustworthiness. The priority given to each of these factors was found to vary according to the criticality and subjectivity of the task.
A series of algorithms have been developed that operationalise three of these factors (expertise, experience, affinity) and generate from various data sources a number of trust metrics for use in social network-based information seeking. The most significant of these data sources is Revyu.com, a reviewing and rating Web site implemented as part of this research, that takes input from regular users and makes it available on the Semantic Web for easy re-use by the implemented algorithms.
Output of the algorithms is used in Hoonoh.com, a Semantic Web-based system that has been developed to support users in identifying relevant and trustworthy information sources within their social networks. Evaluation of this system's ability to predict source selections showed more promising results for the experience factor than for expertise or affinity. This may be attributed to the greater demands these two factors place in terms of input data. Limitations of the work and opportunities for future research are discussed
Credibility-based social network recommendation: Follow the leader
In Web-based social networks (WBSN), social trust relationships between users indicate the similarity of their needs and opinions. Trust can be used to make recommendations on the web because trust information enables the clustering of users based on their credibility which is an aggregation of expertise and trustworthiness. In this paper, we propose a new approach to making recommendations based on leaders' credibility in the "Follow the Leader" model as Top-N recommenders by incorporating social network information into user-based collaborative filtering. To demonstrate the feasibility and effectiveness of "Follow the Leader" as a new approach to making recommendations, first we develop a new analytical tool, Social Network Analysis Studio (SNAS), that captures real data and used it to verify the proposed model using the Epinions dataset. The empirical results demonstrate that our approach is a significantly innovative approach to making effective collaborative filtering based recommendations especially for cold start users. © 2010 Al-Sharawneh & Williams
The Algorithm Game
Most of the discourse on algorithmic decisionmaking, whether it comes in the form of praise or warning, assumes that algorithms apply to a static world. But automated decisionmaking is a dynamic process. Algorithms attempt to estimate some difficult-to-measure quality about a subject using proxies, and the subjects in turn change their behavior in order to game the system and get a better treatment for themselves (or, in some cases, to protest the system.) These behavioral changes can then prompt the algorithm to make corrections. The moves and countermoves create a dance that has great import to the fairness and efficiency of a decision-making process. And this dance can be structured through law. Yet existing law lacks a clear policy vision or even a coherent language to foster productive debate.
This Article provides the foundation. We describe gaming and countergaming strategies using credit scoring, employment markets, criminal investigation, and corporate reputation management as key examples. We then show how the law implicitly promotes or discourages these behaviors, with mixed effects on accuracy, distributional fairness, efficiency, and autonomy
Graph Mining for Cybersecurity: A Survey
The explosive growth of cyber attacks nowadays, such as malware, spam, and
intrusions, caused severe consequences on society. Securing cyberspace has
become an utmost concern for organizations and governments. Traditional Machine
Learning (ML) based methods are extensively used in detecting cyber threats,
but they hardly model the correlations between real-world cyber entities. In
recent years, with the proliferation of graph mining techniques, many
researchers investigated these techniques for capturing correlations between
cyber entities and achieving high performance. It is imperative to summarize
existing graph-based cybersecurity solutions to provide a guide for future
studies. Therefore, as a key contribution of this paper, we provide a
comprehensive review of graph mining for cybersecurity, including an overview
of cybersecurity tasks, the typical graph mining techniques, and the general
process of applying them to cybersecurity, as well as various solutions for
different cybersecurity tasks. For each task, we probe into relevant methods
and highlight the graph types, graph approaches, and task levels in their
modeling. Furthermore, we collect open datasets and toolkits for graph-based
cybersecurity. Finally, we outlook the potential directions of this field for
future research
Business model innovation reshaping the grocery retail
Analisi dell'evoluzione portata nell'ambito del business modelope
TI-CNN: Convolutional Neural Networks for Fake News Detection
With the development of social networks, fake news for various commercial and
political purposes has been appearing in large numbers and gotten widespread in
the online world. With deceptive words, people can get infected by the fake
news very easily and will share them without any fact-checking. For instance,
during the 2016 US president election, various kinds of fake news about the
candidates widely spread through both official news media and the online social
networks. These fake news is usually released to either smear the opponents or
support the candidate on their side. The erroneous information in the fake news
is usually written to motivate the voters' irrational emotion and enthusiasm.
Such kinds of fake news sometimes can bring about devastating effects, and an
important goal in improving the credibility of online social networks is to
identify the fake news timely. In this paper, we propose to study the fake news
detection problem. Automatic fake news identification is extremely hard, since
pure model based fact-checking for news is still an open problem, and few
existing models can be applied to solve the problem. With a thorough
investigation of a fake news data, lots of useful explicit features are
identified from both the text words and images used in the fake news. Besides
the explicit features, there also exist some hidden patterns in the words and
images used in fake news, which can be captured with a set of latent features
extracted via the multiple convolutional layers in our model. A model named as
TI-CNN (Text and Image information based Convolutinal Neural Network) is
proposed in this paper. By projecting the explicit and latent features into a
unified feature space, TI-CNN is trained with both the text and image
information simultaneously. Extensive experiments carried on the real-world
fake news datasets have demonstrate the effectiveness of TI-CNN
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