[Paper Review] False Information on Web and Social Media: A Survey

Srijan Kumar, Neil Shah|arXiv (Cornell University)|Apr 23, 2018

Misinformation and Its Impacts86 references137 citations

TL;DR

This survey reviews how false information spreads on the web and social media, covering actors, rationale, impact, characteristics, and detection methods.

ABSTRACT

False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient detection algorithms and tools for early detection. A recent surge of research in this area has aimed to address the key issues using methods based on feature engineering, graph mining, and information modeling. Majority of the research has primarily focused on two broad categories of false information: opinion-based (e.g., fake reviews), and fact-based (e.g., false news and hoaxes). Therefore, in this work, we present a comprehensive survey spanning diverse aspects of false information, namely (i) the actors involved in spreading false information, (ii) rationale behind successfully deceiving readers, (iii) quantifying the impact of false information, (iv) measuring its characteristics across different dimensions, and finally, (iv) algorithms developed to detect false information. In doing so, we create a unified framework to describe these recent methods and highlight a number of important directions for future research.

Motivation & Objective

• Describe the types and intents of false information on the web and social media.
• Characterize the actors and mechanisms that spread false information at scale.
• Quantify the impact and spread patterns of false information across platforms.
• Review and categorize detection algorithms by feature-based, graph-based, and propagation-modeling approaches.
• Highlight open directions and unified framework for future research.

Proposed method

• Categorize false information by intent (misinformation vs disinformation) and knowledge content (opinion-based vs fact-based).
• Survey actors such as bots and sockpuppets and analyze their roles in amplification and central network positions.
• Synthesize rationale for deception, including human susceptibility, echo chambers, and biases.
• Summarize impact metrics including engagement, longevity, and cross-platform spread.
• Organize detection methods into feature-based, graph-based, and propagation-modeling approaches.

Experimental results

Research questions

• RQ1 What are the main types and intents of false information on the web and social media?
• RQ2 Who are the main actors spreading false information and how do they operate at scale?
• RQ3 What factors explain why readers believe and spread false information?
• RQ4 What is the measured impact and spread pattern of false information across platforms?
• RQ5 What detection approaches exist and how are they categorized and evaluated?

Key findings

• False information can be highly impactful with deep and broad spread and sometimes long survival times.
• Humans are not highly effective at distinguishing false information, even when it is well written or referenced.
• Bots and sockpuppets play key roles in creating perceived consensus and accelerating spread, though human accounts also drive significant share of false information dynamics.
• Echo chambers and confirmation bias contribute to the spread and perceived credibility of false information.
• Detection methods achieving high accuracy exist across feature-based, graph-based, and propagation-modeling paradigms.