基于单点地名匹配和局部地名筛选的推特用户定位方法

doi:10.11959/j.issn.2096-109x.2023053

Abstract

Abstract:

The availability of accurate toponyms in user tweets is crucial for geolocating Twitter users.However, existing methods for locating Twitter users often suffer from limited quantity and reliability of acquired toponyms, thus impacting the accuracy of user geolocation.To address this issue, a twitter user geolocation method based on single-point toponym matching and local toponym filtering was proposed.A toponym type discriminating algorithm based on the aggregation degree of locations of the toponym was designed.In the proposed algorithm, a single-point toponym database was generated to provide more reliable toponyms extracted from tweets.Then, according to a proposed local place name filtering algorithm based on the aggregation degree of user location, the aggregation degree of user location centered on the longitude and latitude of toponyms and the average longitude and latitude of users were calculated.This process helped in extracting local toponyms with a high aggregation degree, which enhances the reliability of toponyms used in geolocation.Finally, a user-toponym heterogeneous graph was constructed based on user social relationships and user mentions of toponyms, and users were located by graph representation learning and neural networks.A large number of user geolocation experiments were conducted based on two commonly used public datasets in this field, namely GEOTEXT and TW-US.Comparisons with nine existing typical methods for Twitter user geolocation, including HGNN, ReLP, and GCN, demonstrate that our proposed method achieves significantly higher geolocation accuracy.On the GEOTEXT dataset, the average error is reduced by 7.3～342.8 km, the median error is reduced by 2.4～354.4 km, and the accuracy of large area-level geolocation is improved by 1.3%～26.3%.On the TW-US dataset, the average error is reduced by 8.6～246.6 km, the median error is reduced by 5.7～149.7 km, and the accuracy of large area-level geolocation is improved by 1.5%～20.5%.

Key words: user geolocation, user-generated text, toponym, social media

CLC Number:

TP391

Jin XUE, Fuxiang YUAN, Yimin LIU, Meng ZHANG, Yaqiong QIAO, Xiangyang LUO. Twitter user geolocation method based on single-point toponym matching and local toponym filtering[J]. Chinese Journal of Network and Information Security, 2023, 9(4): 53-63.

Figures/Tables 10

模型		GEOTEXT数据集			TW-US数据集
模型		大地区级定位准确率	平均误差/km	中位数误差/km	大地区级定位准确率	平均误差/km	中位数误差/km
基于用户	HierLR^[10]	—	—	—	48%	656	191
文本方法	MLP4Geo^[11]	38%	844	389	54%	554	120
基于社交	MADCEL-W^[21]	58%	586	60	54%	705	116
关系方法	GCN-LP^[16]	58%	576	56	53%	563	126
	MENET^[17]	59.1%	570	58	50.5%	474	157
	GCN^[16]	60%	546	45	65%	485	71
多源数据	ReLP^[20]	62%	527	37	67%	418	47
融合方法	MetaGeo^[23]	62%	533	42	63%	479	70
	HGNN^[24]	63%	508.5	37	65%	423.2	59.5
	GEOTOP	$64 . 3 %$	$501 . 2$	$34 . 6$	$68 . 5 %$	$409 . 4$	$41 . 3$

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符号	含义
TP	地名集合
TP ^I	间接地名集合
TP ^D	直接地名集合
LIW	位置指示词集合
k	基于k维树划分后每个位置标签包含的用户数
V^L	有标签用户集合
V^U	目标用户集合
G'	用户-地名图
X	用户推文集合
P^V	用户位置的集合
P^U	目标用户位置的集合
P^L	有标签用户位置的集合
P^TP	地名位置的集合
P	用户和地名的位置集合
f₁	提取直接地名的阈值
f₂	提取间接地名的阈值
s	全局名人过滤阈值

项目	覆盖州数/个	覆盖城市数/个	地名数/个
细粒度地名	50	307	3 035 437
单点地名	50	311	2 043 527

参数	GEOTEXT	TW-US
k	50人	2 400人
输入层维度	128	128
隐藏层维度	256	256
输出层维度	129	256
λ₁	0.5	0.5
λ₂	0.5	0.5
β₁	0.5	0.5
β₂	0.5	0.5
d	150 km	150 km
f₁	0.7	0.75
f₂	0.8	0.82
s	5人	15人
p	4	4
q	0.25	0.25

数据集	训练集用户数/个	验证集用户数/个	测试集用户数/个
GEOTEXT^[7]	5 685	1 895	1 895
TW-US^[25]	1 366 766	10 000	10 000

数据集	用户数/个	推文数/条	用户提及数/个
GEOTEXT^[7]	9 475	377 504	232 598
TW-US^[25]	449 200	38 036 187	8 128 105

Twitter user geolocation method based on single-point toponym matching and local toponym filtering

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地名	指示位置	UL/TL	大地区级定位准确率
paerdegat	40.629, -73.906	1.00	100%
florin mall	38.497, -21.433	1.00	100%
Florida Agricultural and Mechanical University	30.426, -84.285	1.00	100%
suitland	38.848, -76.923	0.91	100%
anaheim stadium	33.8, -117.882	1.00	100%
kearny mesa	32.921, -117.117	0.87	100%
lansing mall	42.743, -84.625	0.86	100%
wheaton mall	38.803, -77.134	0.93	100%

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模型	大地区级定位准确率	平均误差/km	中位数误差/km	覆盖率
第一个实验	61.7%	518.3	54.2	98.2%
第二个实验	82%	83.7	7.4	3.3%
GEOTOP	64.3%	501.2	34.6	98.4%