Detection of suicide-related posts in Twitter data streams

Detection of suicide-related posts in Twitter data streams

ABSTRACT:

Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user’s online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Johnson Vioul_es,B. Moulahi,J. Az_e,S. Bringay, “Detection of suicide-related posts in Twitter data streams”, IEEE, Volume: 62, Issue: 1, Jan.-Feb. 1 2018.

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

ABSTRACT:

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers’ ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Ting Bai, Wanye Xin Zhao Member, IEEE, Yulan He Member, IEEE, Jian-Yun Nie Member, IEEE, Ji-Rong Wen Member, IEEE, “Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites”, IEEE Transactions on Knowledge and Data Engineering, 2018.

Analyzing and Detecting Money-Laundering Accounts in Online Social Networks

Analyzing and Detecting Money-Laundering Accounts in Online Social Networks

ABSTRACT:

Virtual currency in OSNs plays an increasing­ly important role in supporting various financial activities such as currency exchange, online shop­ping, and paid games. Users usually purchase virtual currency using real currency. This fact moti­vates attackers to instrument an army of accounts to collect virtual currency unethically or illegally with no or very low cost and then launder the collected virtual money for massive profit. Such attacks not only introduce significant financial loss of victim users, but also harm the viability of the ecosystem. It is therefore of central importance to detect malicious OSN accounts that engage in laundering virtual currency. To this end, we extensively study the behavior of both malicious and benign accounts based on operation data collected from Tencent QQ, one of the largest OSNs in the world. Then, we devise multi-faceted features that characterize accounts from three aspects: account viability, transaction sequences, and spatial correlation among accounts. Finally, we propose a detection method by integrating these features using a statistical classifier, which can achieve a high detection rate of 94.2 percent at a very low false positive rate of 0.97 percent.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

Yadong Zhou, Ximi Wang, Junjie Zhang, Peng Zhang, Lili Liu, Huan Jin, and Hongbo Jin, “Analyzing and Detecting Money-Laundering Accounts in Online Social Networks”, IEEE Network , Volume: 32, Issue: 3, May/June 2018.

An Efficient Ranked Multi-Keyword Search for Multiple Data Owners

An Efficient Ranked Multi-Keyword Search for Multiple Data Owners Over Encrypted Cloud Data

ABSTRACT:

With the development of cloud storage, more data owners are inclined to outsource their data to cloud services. For privacy concerns, sensitive data should be encrypted before outsourcing. There are various searchable encryption schemes to ensure data availability. However, the existing search schemes pay little attention to the efficiency of data users’ queries, especially for the multi-owner scenario. In this paper, we proposed a tree-based ranked multi-keyword search scheme for multiple data owners. Specifically, by considering a large amount of data in the cloud, we utilize the TF_ IDF model to develop a multikeyword search and return the top-k ranked search results. To enable the cloud servers to perform a secure search without knowing any sensitive data (e.g., keywords and trapdoors), we construct a novel privacy preserving search protocol based on the bilinear mapping. To achieve an efficient search, for each data owner, a tree-based index encrypted with an additive order and privacy-preserving function family is constructed. The cloud server can then merge these indexes effectively, using the depth-first search algorithm to find the corresponding files. Finally, the rigorous security analysis proves that our scheme is secure, and the performance analysis demonstrates its efficacy and efficiency.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : PHP
  • Tool : WAMP
  • Database : MYSQL

REFERENCE:

TIANYUE PENG , (Student Member, IEEE), YAPING LIN, (Member, IEEE), XIN YAO , (Student Member, IEEE), AND WEI ZHANG, “An Efficient Ranked Multi-Keyword Search for Multiple Data Owners Over Encrypted Cloud Data”, IEEE 2018.