Related Experiment Video
Updated: Sep 18, 2025
08:53
Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
Published on: May 31, 2019
5.3K
Quantifying Bot Impact: An Information-Theoretic Analysis of Complexity and Uncertainty in Online Political
1Department of Communication, University of California, 1 Shields Avenue, Davis, CA 95616, USA.
Entropy (Basel, Switzerland)
|June 26, 2025
Summary
Political bots increase complexity and uncertainty in online political discussions. This study uses information theory to analyze bot impacts on communication dynamics, with implications for future AI-driven political landscapes.
Area of Science:
- Computational Social Science
- Information Theory
- Dynamical Systems Theory
Background:
- Bots play an increasing role in shaping democratic processes online.
- Previous research has primarily focused on the individual-level impact of bots, neglecting macro-level communication dynamics.
Purpose of the Study:
- To examine the macro-level impact of political bots on the dynamics of online political discussions.
- To apply an information-theoretic approach from dynamical systems theory to quantify bot influence.
Main Methods:
- Utilized an information-theoretic framework derived from dynamical systems theory.
- Quantified communication dynamics by measuring complexity, predictability, and entropy rate (uncertainty).
- Analyzed bot activity within an online political discussion dataset from Twitter.
Main Results:
- Bot activity was found to be associated with increased complexity in online political communication.
- Bot activity also correlated with greater uncertainty in the structural dynamics of these discussions.
- Findings highlight the potential for sophisticated AI agents to further complicate online political discourse.
Conclusions:
- Bot activity significantly alters the complexity and uncertainty of online political communication dynamics.
- The information-theoretic framework provides a robust method for studying bot impacts.
- Results suggest future online political environments may become more complex and unpredictable due to advanced AI.
Related Concept Videos
00:59Propagation of Uncertainty from Random Error
1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
01:10Propagation of Uncertainty from Systematic Error
897
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
897
02:30Quantifying Work
21.3K
As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system.
21.3K
00:54Uncertainty: Confidence Intervals
4.8K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.8K
00:59Uncertainty: Overview
999
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
999
01:21Causality in Epidemiology
912
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
912