937 research outputs found
Harnessing the power of the general public for crowdsourced business intelligence: a survey
International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI
Detection of spam review on mobile app stores, evaluation of helpfulness of user reviews and extraction of quality aspects using machine learning techniques
As mobile devices have overtaken fixed Internet access, mobile applications and distribution platforms have gained in importance. App stores enable users to search and purchase mobile applications and then to give feedback in the form of reviews and ratings. A review might contain critical information about user experience, feature requests and bug reports. User reviews are valuable not only to developers and software organizations interested in learning the opinion of their customers but also to prospective users who would like to find out what others think about an app.
Even though some surveys have inventoried techniques and methods in opinion mining and sentiment analysis, no systematic literature review (SLR) study had yet reported on mobile app store opinion mining and spam review detection problems. Mining opinions from app store reviews requires pre-processing at the text and content levels, including filtering-out nonopinionated content and evaluating trustworthiness and genuineness of the reviews. In addition, the relevance of the extracted features are not cross-validated with main software engineering concepts.
This research project first conducted a systematic literature review (SLR) on the evaluation of mobile app store opinion mining studies. Next, to fill the identified gaps in the literature, we used a novel convolutional neural network to learn document representation for deceptive spam review detection by characterizing an app store review dataset which includes truthful and spam reviews for the first time in the literature. Our experiments reported that our neural network based method achieved 82.5% accuracy, while a baseline Support Vector Machine (SVM) classification model reached only 70% accuracy despite leveraging various feature combinations.
We next compared four classification models to assess app store user review helpfulness and proposed a predictive model which makes use of review meta-data along with structural and lexical features for helpfulness prediction.
In the last part of this research study, we constructed an annotated app store review dataset for the aspect extraction task, based on ISO 25010 - Systems and software Product Quality Requirements and Evaluation standard and two deep neural network models: Bi-directional Long-Short Term Memory and Conditional Random Field (Bi-LSTM+CRF) and Deep Convolutional Neural Networks and Conditional Random Field (CNN+CRF) for aspect extraction from app store user reviews. Both models achieved nearly 80% F1 score (the weighted average of precision and recall which takes both false positives and false negatives into account) in exact aspect matching and 86% F1 score in partial aspect matching
Fake News Detection with Deep Diffusive Network Model
In recent years, due to the booming development of online social networks,
fake news for various commercial and political purposes has been appearing in
large numbers and widespread in the online world. With deceptive words, online
social network users can get infected by these online fake news easily, which
has brought about tremendous effects on the offline society already. An
important goal in improving the trustworthiness of information in online social
networks is to identify the fake news timely. This paper aims at investigating
the principles, methodologies and algorithms for detecting fake news articles,
creators and subjects from online social networks and evaluating the
corresponding performance. This paper addresses the challenges introduced by
the unknown characteristics of fake news and diverse connections among news
articles, creators and subjects. Based on a detailed data analysis, this paper
introduces a novel automatic fake news credibility inference model, namely
FakeDetector. Based on a set of explicit and latent features extracted from the
textual information, FakeDetector builds a deep diffusive network model to
learn the representations of news articles, creators and subjects
simultaneously. Extensive experiments have been done on a real-world fake news
dataset to compare FakeDetector with several state-of-the-art models, and the
experimental results have demonstrated the effectiveness of the proposed model
Survey on social reputation mechanisms: Someone told me I can trust you
Nowadays, most business and social interactions have moved to the internet,
highlighting the relevance of creating online trust. One way to obtain a
measure of trust is through reputation mechanisms, which record one's past
performance and interactions to generate a reputational value. We observe that
numerous existing reputation mechanisms share similarities with actual social
phenomena; we call such mechanisms 'social reputation mechanisms'. The aim of
this paper is to discuss several social phenomena and map these to existing
social reputation mechanisms in a variety of scopes. First, we focus on
reputation mechanisms in the individual scope, in which everyone is responsible
for their own reputation. Subjective reputational values may be communicated to
different entities in the form of recommendations. Secondly, we discuss social
reputation mechanisms in the acquaintances scope, where one's reputation can be
tied to another through vouching or invite-only networks. Finally, we present
existing social reputation mechanisms in the neighbourhood scope. In such
systems, one's reputation can heavily be affected by the behaviour of others in
their neighbourhood or social group.Comment: 10 pages, 3 figures, 1 tabl
Observation-based Cooperation Enforcement in Ad Hoc Networks
Ad hoc networks rely on the cooperation of the nodes participating in the
network to forward packets for each other. A node may decide not to cooperate
to save its resources while still using the network to relay its traffic. If
too many nodes exhibit this behavior, network performance degrades and
cooperating nodes may find themselves unfairly loaded. Most previous efforts to
counter this behavior have relied on further cooperation between nodes to
exchange reputation information about other nodes. If a node observes another
node not participating correctly, it reports this observation to other nodes
who then take action to avoid being affected and potentially punish the bad
node by refusing to forward its traffic. Unfortunately, such second-hand
reputation information is subject to false accusations and requires maintaining
trust relationships with other nodes. The objective of OCEAN is to avoid this
trust-management machinery and see how far we can get simply by using direct
first-hand observations of other nodes' behavior. We find that, in many
scenarios, OCEAN can do as well as, or even better than, schemes requiring
second-hand reputation exchanges. This encouraging result could possibly help
obviate solutions requiring trust-management for some contexts.Comment: 10 pages, 7 figure
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