Trend Dynamics and Attention in Chinese Social Media
Bibliographic Data
| ID | 3757390 |
|---|---|
| Authors | Louis Lei Yu (Gustavus Adolphus College, St Peter, MN, USA), Louis Yu (Gustavus Adolphus College), Sitaram Asur (Hewlett-Packard Laboratories, Palo Alto, CA, USA), Bernardo A Huberman (0000-0002-6783-0864, Hewlett-Packard Laboratories, Palo Alto, CA, USA) |
| Year | 2015 |
| Volume | 59 |
| Issue | 9 |
| Pages | 1142-1156 |
| Publication date | 2015-08-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Behavioral Scientist (JOURNAL) |
| Journal identifiers | ISSN: 0002-7642 • E-ISSN: 1552-3381 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/0002764215580619 |
| OpenAlex | W2061942100 |
| Language | EN |
| Citations received | 4 |
| References cited | 3 |
We analyzed the temporal aspect of trends and trend-setters in Sina Weibo, contrasting it with earlier observations of Twitter. We found a vast difference in the content shared in China as compared with a global social network such as Twitter. In China, the trends are created almost entirely due to the retweets of media content such as jokes, images, and videos, unlike Twitter where trends have more to do with current global events and news stories. On closer inspection, we observed that most trends in Sina Weibo are due to the continuous retweets of a small percentage of fraudulent accounts, set up to artificially inflate certain posts. This reveals evidence of an "Internet Water Army," a unique promotional method deployed by public relations companies in China to influence the popular dissemination of information in online social networks
Advertising · Business · China · Internet privacy · Microblogging · Political science · Set (abstract data type) · Social media · Social network analysis · The Internet · World Wide Web · Complex Network Analysis Techniques · Computer Science · Opinion Dynamics and Social Influence · Spam and Phishing Detection
| Unique citing works | 4 |
|---|---|
| Citations per year | 0,36 |
| Citation span | 2015 - 2022 (8) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 4 |