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Since the early 2000s, computer-mediated communication (CMC) has grown rapidly, playing a crucial role in self-development. A key distinction between CMC and real-life interactions is the lack of a physically present partner. This absence makes non-verbal cues such as facial expressions, body language, and paralinguistic signals unavailable in CMC platforms like email, instant messaging, or social media. The lack of these cues can create ambiguity and complicate how feedback is interpreted.The...
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Observing Consistency in Online Communication Patterns for User Re-Identification.

Ikuesan Richard Adeyemi1,2, Shukor Abd Razak1, Mazleena Salleh1

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Understanding online human dynamics is key for user identification and profiling. This study shows individuals have unique online behavioral signatures, identifiable using machine learning models like logistic regression.

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Area of Science:

  • Computational Social Science
  • Human-Computer Interaction
  • Machine Learning

Background:

  • Human dynamics in online interactions are complex and crucial for applications like user identification and recommender systems.
  • Existing research has not fully explored the structural composition of online human behavior.
  • Analyzing variations in human activity patterns is essential for understanding online dynamics.

Purpose of the Study:

  • To examine the dimensions of human communication patterns in online interactions.
  • To explore the characteristics of human-driven online communications.
  • To identify methods for distinguishing between online users based on their behavior.

Main Methods:

  • Utilized server-side web data from 31 known users.
  • Employed various machine learning techniques for analysis.
  • Examined interconnectivity and behavioral patterns within the data.

Main Results:

  • Each individual demonstrated a consistent and unique online behavioral signature.
  • Logistic regression and model tree algorithms accurately distinguished between online users.
  • Identified distinct characteristics in human-driven online communications.

Conclusions:

  • Online user identification can be achieved by analyzing unique behavioral signatures.
  • Machine learning models offer effective tools for distinguishing online users.
  • Findings support applications in user identification, insider threat detection, and online profiling.