Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Gayo-Avello, Daniel

doi:10.1016/j.ipm.2013.06.003

Cited by 46 publications

(38 citation statements)

References 30 publications

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“…There are many spammers, also known as 'spambots', sending huge number of tweets. Finding spammers is a very difficult task and a number of researchers (Benevenuto et al 2010;Gayo-Avello 2013;Li and Du 2014;Yardi et al 2010) are focusing on fixing this issue. In our case, spamming does not pose a significant problem, as we were collecting tweets originating from mobile phones only.…”

Section: Data Collectionmentioning

confidence: 99%

Event classification and location prediction from tweets during disasters

et al. 2017

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Social media is a platform to express one's view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

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Section: Data Collectionmentioning

confidence: 99%

Event classification and location prediction from tweets during disasters

et al. 2017

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“…However, there are also other interesting partial surveys in the area. For example, in 2013, Gayo-Avello [44] explained a dozen frequently used measures. These measures have been included here, along with other more than fifty.…”

Section: Introductionmentioning

confidence: 99%

Measuring user influence on Twitter: A survey

Riquelme

González-Cantergiani

2016

Information Processing & Management

367

230

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Abstract. Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.

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“…In addition, it is helpful to improve their situation awareness and development for local events. Daniel Gayo Avello considers that valuable information of Micro-blog can be used as information sources [6]. Miao WANG, Youhua Liu believe that micro-blog can offer information such as focusing on competitors, searching resources, weaving social network, applying tables and making a RSS [7].…”

Section: Discussionmentioning

confidence: 99%

Research on Comparisons of Micro-blog's Document Features

Jin-yuan¹,

Xia²

2016

Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

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Abstract-The paper is to explore Micro-blog's features, namely, whether it belongs to Document, the paper makes comparisons on definition, elements, information processing level, similarities and differences of ten documentary information sources, features and functions between Document and Micro-blog. The paper is through literature investigation or through network research referring to Document's and Micro-blog's various features and functions. The paper concludes that Micro-blog belongs to Document. And its development and growing trend should be paid more attention. The paper is to make suggestions for Microblog's future development and management combining analysis of traditional document restrictions.

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Nepotistic relationships in Twitter and their impact on rank prestige algorithms

Cited by 46 publications

References 30 publications

Event classification and location prediction from tweets during disasters

Event classification and location prediction from tweets during disasters

Measuring user influence on Twitter: A survey

Research on Comparisons of Micro-blog's Document Features

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