Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2019
DOI: 10.1145/3341161.3345024
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Detection of topical influence in social networks via granger-causal inference

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Cited by 2 publications
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“…We collected a dataset consisting of more than 2.5 million Tweets posted across an 8‐year period, from 2007 to 2015, by using the Twitter's Application Program Interface (API). We gathered the Tweets using a Breadth‐First Search (BFS)‐based crawling technique (Hauffa, Koster, Hartl, Kollhofer, & Groh, ; Kwak, Lee, Park, & Moon, ; Macropol, Bogdanov, Singh, Petzold, & Yan, ; Russell, ). The technique, which is like a snowball sampling technique, starts with an initial small set of randomly chosen users.…”
Section: Methodsmentioning
confidence: 99%
“…We collected a dataset consisting of more than 2.5 million Tweets posted across an 8‐year period, from 2007 to 2015, by using the Twitter's Application Program Interface (API). We gathered the Tweets using a Breadth‐First Search (BFS)‐based crawling technique (Hauffa, Koster, Hartl, Kollhofer, & Groh, ; Kwak, Lee, Park, & Moon, ; Macropol, Bogdanov, Singh, Petzold, & Yan, ; Russell, ). The technique, which is like a snowball sampling technique, starts with an initial small set of randomly chosen users.…”
Section: Methodsmentioning
confidence: 99%