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Spatio-Temporal Multiple Geo-Location Identification on Twitter
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0003-1007-8533
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0003-4516-7317
2018 (English)In: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 / [ed] Abe, N Liu, H Pu, C Hu, X Ahmed, N Qiao, M Song, Y Kossmann, D Liu, B Lee, K Tang, J He, J Saltz, J, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 3412-3421Conference paper, Published paper (Refereed)
Abstract [en]

Twitter Geo-tags that indicate the exact location of messages have many applications from localized opinion mining during elections to efficient traffic management in critical situations. However, less than 6% of Tweets are Geo-tagged, which limits the implementation of those applications. There are two groups of solutions: content and network-based. The first group uses location indicative factors like URLs and topics, extracted from the content of tweets, to infer Geo-location for non geoactive users, whereas the second group benefits from friendship ties in the underlying social network graph. Friendship ties are better predictors compared to content information because they are less noisy and often follow the natural human spatial movement patterns. However, their prediction's accuracy is still limited because they ignore the temporal aspects of human behavior and always assume a single location per user. This research aims to extend the current network-based approaches by taking users' temporal dimension into account. We assume multiple locations per user during different time-slots and hypothesize that location predictability varies depending on the time and the properties of the social membership group. Thus, we propose a hierarchical solution to apply temporal categorizations on top of social network partitioning for multiple location prediction for users in Online Social Networks (OSNs) like Twitter. Given a largescale Twitter dataset, we show that users' location predictability exhibits different behavior in different time-slots and different social groups. We find that there are specific conditions where users are more predictable in terms of Geo-location. Our solution outperforms the state-of-the-art by improving the prediction accuracy by 16:6% in terms of Median Error Distance (MED) over the same recall.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 3412-3421
Series
IEEE International Conference on Big Data, ISSN 2639-1589
Keywords [en]
Geo-Location Identification, Graph Partitioning, Social Network Analysis, Spatio-Temporal Analysis
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254147DOI: 10.1109/BigData.2018.8621997ISI: 000468499303064Scopus ID: 2-s2.0-85062605032ISBN: 978-1-5386-5035-6 (print)OAI: oai:DiVA.org:kth-254147DiVA, id: diva2:1329207
Conference
2018 IEEE International Conference on Big Data, Big Data 2018; Seattle; United States; 10 December 2018 through 13 December 2018
Note

QC 20190624

Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-11-19Bibliographically approved
In thesis
1. Graph Algorithms for Large-Scale and Dynamic Natural Language Processing
Open this publication in new window or tab >>Graph Algorithms for Large-Scale and Dynamic Natural Language Processing
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In Natural Language Processing, researchers design and develop algorithms to enable machines to understand and analyze human language. These algorithms benefit multiple downstream applications including sentiment analysis, automatic translation, automatic question answering, and text summarization. Topic modeling is one such algorithm that solves the problem of categorizing documents into multiple groups with the goal of maximizing the intra-group document similarity. However, the manifestation of short texts like tweets, snippets, comments, and forum posts as the dominant source of text in our daily interactions and communications, as well as being the main medium for news reporting and dissemination, increases the complexity of the problem due to scalability, sparsity, and dynamicity. Scalability refers to the volume of the messages being generated, sparsity is related to the length of the messages, and dynamicity is associated with the ratio of changes in the content and topical structure of the messages (e.g., the emergence of new phrases). We improve the scalability and accuracy of Natural Language Processing algorithms from three perspectives, by leveraging on innovative graph modeling and graph partitioning algorithms, incremental dimensionality reduction techniques, and rich language modeling methods. We begin by presenting a solution for multiple disambiguation on short messages, as opposed to traditional single disambiguation. The solution proposes a simple graph representation model to present topical structures in the form of dense partitions in that graph and applies disambiguation by extracting those topical structures using an innovative distributed graph partitioning algorithm. Next, we develop a scalable topic modeling algorithm using a novel dense graph representation and an efficient graph partitioning algorithm. Then, we analyze the effect of temporal dimension to understand the dynamicity in online social networks and present a solution for geo-localization of users in Twitter using a hierarchical model that combines partitioning of the underlying social network graph with temporal categorization of the tweets. The results show the effect of temporal dynamicity on users’ spatial behavior. This result leads to design and development of a dynamic topic modeling solution, involving an online graph partitioning algorithm and a significantly stronger language modeling approach based on the skip-gram technique. The algorithm shows strong improvement on scalability and accuracy compared to the state-of-the-art models. Finally, we describe a dynamic graph-based representation learning algorithm that modifies the partitioning algorithm to develop a generalization of our previous work. A strong representation learning algorithm is proposed that can be used for extracting high quality distributed and continuous representations out of any sequential data with local and hierarchical structural properties similar to natural language text.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2019. p. 42
Series
TRITA-EECS-AVL ; 2019:85
Keywords
Natural Language Processing; Lexical Disambiguation; Topic Modeling; Representation Learning; Graph Partitioning; Distributed Algorithms; Dimensionality Reduction; Random Indexing;
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-263914 (URN)978-91-7873-377-4 (ISBN)
Public defence
2019-12-17, Sal C, Electrum, Kistagången 16, Kista, 10:00 (English)
Opponent
Supervisors
Note

QC 20191125

Available from: 2019-11-25 Created: 2019-11-19 Last updated: 2019-11-25Bibliographically approved

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Ghoorchian, KambizGirdzijauskas, Sarunas

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