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ACM Transactions on the Web (TWEB)
Journal Prestige (SJR): 0.426

Citation Impact (citeScore): 3
Number of Followers: 3

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1559-1131 - ISSN (Online) 1559-114X
Published by ACM

[83 journals]

Cross-site Prediction on Social Influence for Cold-start Users in Online
Social Networks
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  Abstract: Qingyuan Gong, Yang Chen, Xinlei He, Yu Xiao, Pan Hui, Xin Wang, Xiaoming Fu
  
  Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs.
  PubDate: Wed, 12 May 2021 00:00:00 GMT
An Integrated Approach for Improving Brand Consistency of Web Content:
Modeling, Analysis, and Recommendation
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  Abstract: Soumyadeep Roy, Shamik Sural, Niyati Chhaya, Anandhavelu Natarajan, Niloy Ganguly
  
  A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed the brand personality of the company. The perception is impressed upon the consumer through the content, be it in the form of advertisement, blogs, or magazines, produced by the organization. A consistent brand will generate trust and retain customers over time as they develop an affinity toward regularity and common patterns. However, maintaining a consistent messaging tone for a brand has become more challenging with the virtual explosion in the amount of content that needs to be authored and pushed to the Internet to maintain an edge in the era of digital marketing.
  PubDate: Tue, 04 May 2021 00:00:00 GMT
ACM Transactions on the Web (TWEB) Volume 15 Issue 2, May 2021
(Issue-in-Progress)
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  PubDate: Sat, 01 May 2021 00:00:00 GMT
A Two Phases Self-healing Framework for Service-oriented Systems
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  Abstract: Amal Alhosban, Zaki Malik, Khayyam Hashmi, Brahim Medjahed, Hassan Al-Ababneh
  
  Service-Oriented Architectures (SOA) enable the automatic creation of business applications from independently developed and deployed Web services. As Web services are inherently a priori unknown, how to deliver reliable Web services compositions is a significant and challenging problem. Services involved in an SOA often do not operate under a single processing environment and need to communicate using different protocols over a network. Under such conditions, designing a fault management system that is both efficient and extensible is a challenging task. In this article, we propose SFSS, a self-healing framework for SOA fault management. SFSS is predicting, identifying, and solving faults in SOAs.
  PubDate: Thu, 22 Apr 2021 00:00:00 GMT
Utilizing Web Trackers for Sybil Defense
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  Abstract: Marcel Flores, Andrew Kahn, Marc Warrior, Alan Mislove, Aleksandar Kuzmanovic
  
  User tracking has become ubiquitous practice on the Web, allowing services to recommend behaviorally targeted content to users. In this article, we design Alibi, a system that utilizes such readily available personalized content, generated by recommendation engines in real time, as a means to tame Sybil attacks. In particular, by using ads and other tracker-generated recommendations as implicit user “certificates,” Alibi is capable of creating meta-profiles that allow for rapid and inexpensive validation of users’ uniqueness, thereby enabling an Internet-wide Sybil defense service. We demonstrate the feasibility of such a system, exploring the aggregate behavior of recommendation engines on the Web and demonstrating the richness of the meta-profile space defined by such inputs.
  PubDate: Thu, 22 Apr 2021 00:00:00 GMT
Exploring Weather Data to Predict Activity Attendance in Event-based
Social Network: From the Organizer’s View
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  Abstract: Jifeng Zhang, Wenjun Jiang, Jinrui Zhang, Jie Wu, Guojun Wang
  
  Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities.
  PubDate: Thu, 22 Apr 2021 00:00:00 GMT

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