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SocialSearch: enhancing entity search with social network matching

Published: 21 March 2011 Publication History

Abstract

This paper introduces the problem of matching people names to their corresponding social network identities such as their Twitter accounts. Existing tools for this purpose build upon naive textual matching and inevitably suffer low precision, due to false positives (e.g., fake impersonator accounts) and false negatives (e.g., accounts using nicknames). To overcome these limitations, we leverage "relational" evidences extracted from the Web corpus. In particular, as such an example, weadopt Web document co-occurrences, which can be interpreted as an "implicit" counterpart of Twitter follower relationships. Using both textual and relational features, we learn a ranking function aggregating these features for the accurate ordering of candidate matches. Another key contribution of this paper is to formulate confidence scoring as a separate problem from relevance ranking. A baseline approach is to use the relevance of the top match itself as the confidence score. In contrast, we train a separate classifier, using not only the top relevance score but also various statistical features extracted from the relevance scores of all candidates, and empirically validate to outperform the baseline approach. We evaluate our proposed system using real-life internetscale entity-relationship and social network graphs.

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  • (2025)Network alignmentPhysics Reports10.1016/j.physrep.2024.11.0061107(1-45)Online publication date: Mar-2025
  • (2023)Cross-Graph Embedding With Trainable Proximity for Graph AlignmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327011935:12(12556-12570)Online publication date: 1-Dec-2023
  • (2022)Multi-Source Spatial Entity LinkageIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299049134:3(1344-1358)Online publication date: 1-Mar-2022
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      cover image ACM Other conferences
      EDBT/ICDT '11: Proceedings of the 14th International Conference on Extending Database Technology
      March 2011
      587 pages
      ISBN:9781450305280
      DOI:10.1145/1951365
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 21 March 2011

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      Author Tags

      1. entity search
      2. graph matching
      3. social network

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      • Microsoft Research
      EDBT/ICDT '11: EDBT/ICDT '11 joint conference
      March 21 - 24, 2011
      Uppsala, Sweden

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      Overall Acceptance Rate 7 of 10 submissions, 70%

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      Cited By

      View all
      • (2025)Network alignmentPhysics Reports10.1016/j.physrep.2024.11.0061107(1-45)Online publication date: Mar-2025
      • (2023)Cross-Graph Embedding With Trainable Proximity for Graph AlignmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327011935:12(12556-12570)Online publication date: 1-Dec-2023
      • (2022)Multi-Source Spatial Entity LinkageIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299049134:3(1344-1358)Online publication date: 1-Mar-2022
      • (2022)Research on the Relationship Between Chinese Nicknames and Accounts in Social NetworksCyber Security10.1007/978-981-16-9229-1_9(143-156)Online publication date: 21-Jan-2022
      • (2020)A multiview approach based on naming behavioral modeling for aligning chinese user accounts across multiple networksConcurrency and Computation: Practice and Experience10.1002/cpe.581932:22Online publication date: 5-Aug-2020
      • (2018)User identification across online social networks in practiceJournal of Information Science10.1177/016555151667348044:3(377-391)Online publication date: 1-Jun-2018
      • (2017)A Method of Identifying User Identity Based on Username FeaturesInternational Journal of Handheld Computing Research10.4018/IJHCR.20171001018:4(1-22)Online publication date: 1-Oct-2017
      • (2017)Active Learning for Large-Scale Entity ResolutionProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132949(1379-1388)Online publication date: 6-Nov-2017
      • (2017)Identity vs. Attribute Disclosure Risks for Users with Multiple Social ProfilesProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3110046(163-170)Online publication date: 31-Jul-2017
      • (2017)A Solution to Tweet-Based User Identification Across Online Social NetworksAdvanced Data Mining and Applications10.1007/978-3-319-69179-4_18(257-269)Online publication date: 14-Oct-2017
      • Show More Cited By

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