基于多种提及关系的社交媒体用户位置推断

doi:10.11959/j.issn.1000-436X.2020229

Abstract

Abstract:

Aiming at the problem that the existing joint user geolocalization methods based on social media text and social relationships do not sufficiently mine the location correlation between heterogeneous data in social media, a social media user geolocalization method based on multiple mention relationships was proposed.First, a heterogeneous network was constructed by comprehensively considering the mention relationship between users, the user's mention relationship with location indicative words, and the user's mention relationship with geographic nouns.Then, a network simplification strategy was proposed to construct a user-location heterogeneous network that connects users live nearby more closely based on the common mention relationship.After that, a biased random walk algorithm was proposed for the graph node sampling to fully explore the network structure and alleviate the sparsity problem of known locations.Finally, a neural network classifier based on a multilayer perceptron was used to infer the user's location.Experimental results on three representative Twitter data sets of GEOTEXT, TW-US and TW-WORLD show that the proposed method can significantly improve the user geolocalization accuracy.

Key words: social media, heterogeneous network, user geolocalization, mention relationship

CLC Number:

TP391

Yaqiong QIAO, Xiangyang LUO, Jiangtao MA, Chenliang LI, Meng ZHANG, Ruixiang LI. Social media user geolocalization based on multiple mention relationships[J]. Journal on Communications, 2020, 41(12): 72-81.

Figures/Tables 8

位置标签	经度	纬度				地理名词
l₇	-111.88°	33.39°	$A r i z o n a$	softball	bubbles	josh	cracker	johnson	pissing
l₂₀	-97.30°	32.63°	$A u s t i n$	$D a l l a s$	coconut	fallon	badu	chorus	jamming
l₂₃	-96.08°	39.7°	$k a n s a s$	lambert	machines	insomnia	mayo	guts	slang
l₂₉	-87.63°	41.73°	$C h i c a g o$	$C h i$	lbs	peppers	greg	julius	dez
l₅₅	-82.29°	39.32°	$C o l u m b u s$	$n a s h v i l l e$	sangria	gummy	graphic	ditto	lauryn

方法	Acc@161	mean/km	median/km	覆盖率
GCN^[15]	60%	546	45	95.52%
GELP-MEW	38%	996	465	$99 . 86 %$
GELP-MW	55%	683	79	$99 . 86 %$
GELP-I	60%	553	48	$99 . 86 %$
GELP-UW	63%	499	$38$	98.28%
GELP	$64 %$	$494$	$38$	98.28%

References 29

[1]	KAWANAKA S , MORIWAKI D . Uplift modeling for location-based online advertising[C]// Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations,Geosocial Networks and Geoadvertising. New York:ACM Press, 2019: 1-4.
[2]	SHAHRAKI Z K , FATEMI A , MALAZI H T . Evidential fine-grained event localization using Twitter[J]. Information Processing ＆ Management, 2019,55(6): 102045.1-102045.20
[3]	ZHOU N , ZHAO W X , ZHANG X ,et al. A general multi-context embedding model for mining human trajectory data[J]. IEEE Transactions on Knowledge ＆ Data Engineering, 2016,28(8): 1945-1958.
[4]	张文静, 刘樵, 朱辉 ,等. 基于信息论方法的多等级位置隐私度量与保护[J]. 通信学报, 2019,40(12): 51-59.
	ZHANG W J , LIU Q , ZHU H ,et al. Evaluation and protection of mul-ti-level location privacy based on an information theoretic approach[J]. Journal on Communications, 2019,40(12): 51-59.
[5]	李维皓, 曹进, 李晖 ,等. 基于位置服务隐私自关联的隐私保护方案[J]. 通信学报, 2019,40(5): 57-66.
	LI W H , CAO J , LI H ,et al. Privacy self-correlation privacy-preserving scheme in LBS[J]. Journal on Communications, 2019,40(5): 57-66.
[6]	POULSTON A , STEVENSON M , BONTCHEVA K . Hyperlocal home location identification of Twitter profiles[C]// Proceedings of the 28th ACM Conference on Hypertext and Social Media. New York:ACM Press, 2017: 45-54.
[7]	EISENSTEIN J , O’CONNOR B , SMITH N A ,et al. A latent variable model for geographic lexical variation[C]// Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2010: 1277-1287.
[8]	AHMED A , HONG L , SMOLA A J . Hierarchical geographical modeling of user locations from social media posts[C]// Proceedings of the 22nd International World Wide Web Conference. New York:ACM Press, 2013: 25-36.
[9]	WING B , BALDRIDGE J . Hierarchical discriminative classification for text-based geolocation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2014: 336-348.
[10]	SCHULZ A , HADJAKOS A , PAULHEIM H ,et al. A multi-indicator approach for geolocalization of tweets[C]// Proceedings of the Seventh International Conference on Weblogs and Social Media. Palo Alto:AAAI Press, 2013: 573-582.
[11]	RAHIMI A , VU D , COHN T ,et al. Exploiting text and network context for geolocation of social media users[C]// The 2015 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg:Association for Computational Linguistics, 2015: 1362-1367.
[12]	RAHIMI A , COHN T , BALDWIN T . A neural model for user geolocation and lexical dialectology[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:Association for Computational Linguistics, 2017,2: 209-216.
[13]	JURGENS D , . That’s what friends are for:inferring location in online social media platforms based on social relationships[C]// Proceedings of the Seventh International Conference on Weblogs and Social Media. Palo Alto:AAAI Press, 2013: 273-282.
[14]	RAHIMI A , COHN T , BALDWIN T . Twitter user geolocation using a unified text and network prediction model[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2015: 630-636.
[15]	RAHIMI A , COHN T , BALDWIN T . Semi-supervised user geolocation via graph convolutional networks[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:Association for Computational Linguistics, 2018,1(1): 2009-2019.
[16]	DO T H , NGUYEN D M , TSILIGIANNI E ,et al. Twitter user geolocation using deep multiview learning[C]// 2018 IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2018: 6304-6308.
[17]	ZHONG T , WANG T , WANG J ,et al. Multiple-aspect attentional graph neural networks for online social network user localization[J]. IEEE Access, 2020,8: 95223-95234.
[18]	ROLLER S , SPERIOSU M , RALLAPALLI S ,et al. Supervised text-based geolocation using language models on an adaptive grid[C]// Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Stroudsburg:Association for Computational Linguistics, 2012: 1500-1510.
[19]	HAN B , COOK P , BALDWIN T . Text-based twitter user geolocation prediction[J]. Journal of Artificial Intelligence Research, 2014,49(1): 451-500.
[20]	KRISHNAMURTHY R . Knowledge enabled location prediction of Twitter users[D]. Dayton:Wright State University, 2015.
[21]	CHENG Z , CAVERLEE J , LEE K . You are where you tweet:a content-based approach to geo-locating twitter users[C]// Proceedings of the 19th ACM Conference on Information and Knowledge Management. New York:ACM Press, 2010: 759-768.
[22]	ZHENG X , HAN J , SUN A . A survey of location prediction on Twitter[J]. IEEE Transactions on Knowledge ＆ Data Engineering, 2018,30(9): 1652-1671.
[23]	WING B , BALDRIDGE J . Simple supervised document geolocation with geodesic grids[C]// Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies. Stroudsburg:Association for Computational Linguistics, 2011: 955-964.
[24]	GROVER A , LESKOVEC J . Node2Vec:scalable feature learning for networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2016: 855-864.
[25]	MIKOLOV T , SUTSKEVER I , CHEN K ,et al. Distributed representations of words and phrases and their compositionality[J]. Advances in Neural Information Processing Systems, 2013,26(2): 3111-3119.
[26]	NAIR V , HINTON G E . Rectified linear units improve restricted boltzmann machines vinod nair[C]// Proceedings of the 27th International Conference on Machine Learning. Madison:Omni Press, 2010, 807-814.
[27]	AL-RFOU R , ALAIN G , ALMAHAIRI A ,et al. Theano:a Python framework for fast computation of mathematical expressions[J]. arXiv e-prints,1605.02688, 2016: 1-19.
[28]	KINGMA D P , BA J . Adam:a method for stochastic optimization[J]. arXiv Preprint,arXiv:1412.6980, 2014.
[29]	HAN B , COOK P , BALDWIN T . Geolocation Prediction in Social Media Data by Finding Location Indicative Words[C]// Proceedings of the 24th International Conference on Computational Linguistics. Mumbai:The COLING 2012 Organizing Committee, 2012: 1045-1062.

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数据集	推文数/个	用户提及数/个	用户总数/个	训练集用户数/个	测试集用户数/个	开发集用户数/个	每个用户推文数/条
GEOTEXT	378×10³	109×10³	9 475	5 685	1 895	1 895	39
TW-US	38×10⁶	3.63×10⁶	450×10³	430×10³	10×10³	10×10³	84
TW-WORLD	12×10⁶	16.8×10⁶	1.4×10⁶	1.38×10⁶	10×10³	10×10³	9

数据来源	方法		GEOTEXT			TW-US			TW-WORLD
数据来源	方法	Acc@161	mean/km	median/km	Acc@161/km	mean/km	median/km	Acc@161	mean/km	median
	HierLR+k-d tree^[9]	—	—	—	48%	686	191	31%	1 669	509
Text	(MLP + k-d tree)^[12]	38%	844	389	54%	554	120	34%	1 456	415
	LR^[11]	38%	880	397	50%	686	159	32%	1 724	450
Network	MADCEL-W^[14]	58%	586	60	54%	705	116	45%	2 525	279
	GCN-LP^[15]	58%	576	56	53%	563	126	45%	2 357	279
	MLP-TXT+NET^[15]	58%	554	58	66%	420	56	58%	1 030	53
	MADCEL-W-MLP^[12]	59%	578	61	61%	515	77	53%	1 280	104
Text+Network	GCN^[15]	60%	546	45	62%	485	71	54%	1 130	108
	MAGNN^[17]	62%	514	38	63%	452	67	55%	1 107	102
	GELP	64%	489	39	67%	430	44	59%	1 015	50

Social media user geolocalization based on multiple mention relationships

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