Distinguishing Individuals from Organisations on Twitter

Kim, Sunghwan Mac; Paris, Cécile; Power, Robert; Wan, Stephen

doi:10.1145/3041021.3054217

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…We also use the subset of data from Indonesian Twitter gender prediction experiment [49], in which ground truth location exists. We categorized all the users into two types: person and nonperson [50][51][52]. The person and nonperson accounts were balanced in each location class.…”

Section: Data Collection and Annotationmentioning

confidence: 99%

We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model

Simanjuntak

Mahendra

Yulianti

2022

BDCC

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Twitter user location data provide essential information that can be used for various purposes. However, user location is not easy to identify because many profiles omit this information, or users enter data that do not correspond to their actual locations. Several related works attempted to predict location on English-language tweets. In this study, we attempted to predict the location of Indonesian tweets. We utilized machine learning approaches, i.e., long-short term memory (LSTM) and bidirectional encoder representations from transformers (BERT) to infer Twitter users’ home locations using display name in profile, user description, and user tweets. By concatenating display name, description, and aggregated tweet, the model achieved the best accuracy of 0.77. The performance of the IndoBERT model outperformed several baseline models.

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Section: Data Collection and Annotationmentioning

confidence: 99%

We Know You Are Living in Bali: Location Prediction of Twitter Users Using BERT Language Model

Simanjuntak

Mahendra

Yulianti

2022

BDCC

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“…Recently, research on incorporating heterogeneous information into NRL models has received increasing interests [23], [24], [25], [26]. Among them, specific text information (e.g., labelling information) is usually used as edges' external information for node representation.…”

Section: Network Representation Learningmentioning

confidence: 99%

Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining

Liu

Xia

Wang

et al. 2019

IEEE Trans. Knowl. Data Eng.

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The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.

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