2013
DOI: 10.1007/978-3-642-37401-2_14
|View full text |Cite
|
Sign up to set email alerts
|

Detecting Event Rumors on Sina Weibo Automatically

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

1
54
0
2

Year Published

2015
2015
2021
2021

Publication Types

Select...
4
4
1

Relationship

0
9

Authors

Journals

citations
Cited by 92 publications
(57 citation statements)
references
References 9 publications
1
54
0
2
Order By: Relevance
“…At the end of this phase, our labeled data set 5 consists of 2601 false rumors, 2536 normal messages and with 4 million distinct users involved in these messages. Of these 500 false rumors and 500 other messages (called small data set) are used 5 The labeled data set of the original messages (without reposts) is available at http://adapt.seiee.sjtu.edu.cn/ ∼ kzhu/rumor/.…”
Section: A Datasetmentioning
confidence: 99%
See 1 more Smart Citation
“…At the end of this phase, our labeled data set 5 consists of 2601 false rumors, 2536 normal messages and with 4 million distinct users involved in these messages. Of these 500 false rumors and 500 other messages (called small data set) are used 5 The labeled data set of the original messages (without reposts) is available at http://adapt.seiee.sjtu.edu.cn/ ∼ kzhu/rumor/.…”
Section: A Datasetmentioning
confidence: 99%
“…While some researchers have worked previously on Twitter (see Section V), this paper focuses on false rumor detection on Sina Weibo, for which only limited research has been done [4], [5], [6]. Automatic detection of false rumors is generally a hard problem, because without proper background knowledge or concrete, official evidence against, even human being cannot distinguish between the false rumor and other messages.…”
Section: Introductionmentioning
confidence: 99%
“…Similarly, Rubin et al [29] use satirical cues to detect fakes, which only applies to a specific subset of cases. Another category of methods attempt to include image features in the classification, under the assumption that the image accompanying a post may carry distinct visual characteristics that differ between fake and real posts [14,34]. While this assumption may hold true when contrasting verified posts by news agencies to fake posts by unverified sources, it certainly cannot assist us when comparing user-generated fake and real posts.…”
Section: Related Workmentioning
confidence: 99%
“…Sun Shengyun et.al proposed an idea of using external knowledge to detect specific rumors. Aiming at the incongruent of text and picture problem in rumor, they searched the picture source via search engine and trained classifier to detect rumors [13].…”
Section: Related Workmentioning
confidence: 99%