Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- 2 The Problem of Sentiment Analysis
- 3 Document Sentiment Classification
- 4 Sentence Subjectivity and Sentiment Classification
- 5 Aspect Sentiment Classification
- 6 Aspect and Entity Extraction
- 7 Sentiment Lexicon Generation
- 8 Analysis of Comparative Opinions
- 9 Opinion Summarization and Search
- 10 Analysis of Debates and Comments
- 11 Mining Intentions
- 12 Detecting Fake or Deceptive Opinions
- 13 Quality of Reviews
- 14 Conclusions
- Appendix
- Bibliography
- Index
12 - Detecting Fake or Deceptive Opinions
Published online by Cambridge University Press: 05 June 2015
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- 2 The Problem of Sentiment Analysis
- 3 Document Sentiment Classification
- 4 Sentence Subjectivity and Sentiment Classification
- 5 Aspect Sentiment Classification
- 6 Aspect and Entity Extraction
- 7 Sentiment Lexicon Generation
- 8 Analysis of Comparative Opinions
- 9 Opinion Summarization and Search
- 10 Analysis of Debates and Comments
- 11 Mining Intentions
- 12 Detecting Fake or Deceptive Opinions
- 13 Quality of Reviews
- 14 Conclusions
- Appendix
- Bibliography
- Index
Summary
Opinions from social media are increasingly used by individuals and organizations for making purchase decisions, making choices at elections, and for marketing and product design. Positive opinions often mean profits and fame for businesses and individuals. This, unfortunately, gives strong incentives for imposters to game the system by posting fake reviews or opinions to promote or to discredit some target products, services, organizations, individuals, and even ideas without disclosing their true intentions, or the person or organization that they are secretly working for. Such individuals are called opinion spammers and their activities are called opinion spamming (Jindal and Liu, 2007, 2008). An opinion spammer is also called a shill, a plant, or a stooge in the social media environment, and opinion spamming is also called shilling or astroturfing. Opinion spamming not only can hurt consumers and damage businesses, but also can warp opinions and mobilize masses into positions counter to legal or ethical mores. This can be frightening, especially when spamming is about opinions on social and political issues. It is safe to say that as opinions in social media are increasingly used in practice, opinion spamming is becoming more and more sophisticated, which presents a major challenge for their detection. However, they must be detected to ensure that the social media continues to be a trusted source of public opinions, rather than being full of fakes, lies, and deceptions. The good news is that both the industry and the research community have made tremendous progress in combating opinion spamming. I am aware that several major review hosting sites are able to detect a good proportion of fake reviews and fake reviewers. These efforts have already acted as a deterrent to opinion spamming and made it difficult for inexperienced spammers to succeed. However, the problem is still huge and a great deal of research is needed.
Spam detection in general has been studied in many fields. Web spam and e-mail spam are perhaps the two most widely studied types of spam. Opinion spam is, however, very different.
- Type
- Chapter
- Information
- Sentiment AnalysisMining Opinions, Sentiments, and Emotions, pp. 259 - 302Publisher: Cambridge University PressPrint publication year: 2015