Bayesian identification of bots using temporal analysis of tweet storms
Bibliographic Data
| ID | 16859044 |
|---|---|
| Authors | Spencer Lee Kirn (0000-0001-6308-3921, William & Mary), Mark K Hinders (0000-0003-0846-9535, William & Mary, corresponding author) |
| Year | 2021 |
| Volume | 11 |
| Issue | 1 |
| Publication date | 2021-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-021-00783-7 |
| OpenAlex | W3195374451 |
| Language | EN |
| Citations received | 1 |
| References cited | 53 |
Artificial neural network · Machine learning · Metadata · Naive Bayes classifier · Perceptron · Support vector machine · Complex Network Analysis Techniques · Computer Science · Misinformation and Its Impacts · Spam and Phishing Detection · Artificial Intelligence
BotOrNot
Analyzing the Digital Traces of Political Manipulation
The rise of social bots
Online Human-Bot Interactions
Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network
Arming the public with artificial intelligence to counter social bots
Identifying and Analyzing Health-Related Themes in Disinformation Shared by Conservative and Liberal Russian Trolls on Twitter
Social bots distort the 2016 U.S. Presidential election online discussion
Automating power
Efficient detection of online communities and social bot activity during electoral campaigns
Weaponized Health Communication
Leveraging node neighborhoods and egograph topology for better bot detection in social graphs
Probabilistic reasoning system for social influence analysis in online social networks
Dynamic wavelet fingerprint for differentiation of tweet storm types
| Citations per year | 0.25 |
|---|---|
| Citation span | 2022 - 2022 (1) |
| Highly cited | No |