scholarly journals User Identification Based on Integrating Multiple User Information across Online Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wenjing Zeng ◽  
Rui Tang ◽  
Haizhou Wang ◽  
Xingshu Chen ◽  
Wenxian Wang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
User Privacy ◽  
User Identification ◽  
Multiple User ◽  
Information User ◽  
User Data ◽  
The Impact

User identification can help us build more comprehensive user information. It has been attracting much attention from academia. Most of the existing works are profile-based user identification and relationship-based user identification. Due to user privacy settings and social network restrictions on user data crawl, user data may be missing or incomplete in real social networks. User data include profiles, user-generated contents (UGCs), and relationships. The features extracted in previous research may be sparse. In order to reduce the impact of the above problems on user identification, we propose a multiple user information user identification framework (MUIUI). Firstly, we develop multiprocess crawlers to obtain the user data from two popular social networks, Twitter and Facebook. Secondly, we use named entity recognition and entity linking to obtain and integrate locations and organizations from profiles and UGCs. We also extract URLs from profiles and UGCs. We apply the locations jointly with the relationships and develop several algorithms to measure the similarity of the display name, all locations, all organizations, location in profile, all URLs, following organizations, and user ID, respectively. Afterward, we propose a fusion classifier machine learning-based user identification method. The results show that the F1 score of MUIUI reaches 86.46% on the dataset. It proves that MUIUI can reduce the impact of user data that are missing or incomplete.

scholarly journals Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data

2018 ◽  
Author(s):  
Aldo Hernandez-Suarez ◽  
Gabriel Sanchez-Perez ◽  
Karina Toscano-Medina ◽  
Hector Perez-Meana ◽  
Jose Portillo-Portillo ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Named Entity Recognition ◽  
Name Entity Recognition ◽  
Spatial Modelling ◽  
Entity Recognition ◽  
Language Models ◽  
Geographic Space ◽  
Named Entity ◽  
Estimation Function

In recent years, online social networks have received important consideration in spatial modelling fields given the critical information that can be extracted from them for events in real time; one of the most latent issues is that regarding various natural disasters such as earthquakes. Although it is possible to retrieve data from these social networks with embedded geographic information provided by GPS, in many cases this is not possible. An alternative solution is to reconstruct specific locations using probabilistic language models, more specifically those based on Name Entity Recognition (NER), which extracts names from a user’s description about an event occurring in a specific place (e.g., a collapsed building on a specific avenue). In this work, we present a methodology to use twitter as a social sensor system for disasters. The methodology scores NER locations with a kernel density estimation function for different subtopics originating from a natural disaster and that maps them into a geographic space is proposed. The proposed methodology is evaluated with tweets related to the 2017 earthquake in Mexico.

User identification across online social networks in practice: Pitfalls and solutions

2016 ◽  
Vol 44 (3) ◽  
pp. 377-391 ◽  
Author(s):  
Azadeh Esfandyari ◽  
Matteo Zignani ◽  
Sabrina Gaito ◽  
Gian Paolo Rossi
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Full Range ◽  
Application Programming Interface ◽  
User Identification ◽  
Application Programming ◽  
Test Sets ◽  
Set Up ◽  
Different Levels ◽  
Programming Interface

To take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter.

scholarly journals Semi-Supervised Noisy Label Learning for Chinese Medical Named Entity Recognition

2021 ◽  
pp. 1-10
Author(s):  
Zhucong Li ◽  
Zhen Gan ◽  
Baoli Zhang ◽  
Yubo Chen ◽  
Jing Wan ◽  
...  
Keyword(s):  
Hybrid System ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Final Test ◽  
Named Entity ◽  
The Core ◽  
Category Labels ◽  
Adversarial Training ◽  
Core Idea ◽  
The Impact

Abstract This paper describes our approach for the Chinese Medical named entity recognition(MER) task organized by the 2020 China conference on knowledge graph and semantic computing(CCKS) competition. In this task, we need to identify the entity boundary and category labels of six entities from Chinese electronic medical record(EMR). We construct a hybrid system composed of a semi-supervised noisy label learning model based on adversarial training and a rule postprocessing module. The core idea of the hybrid system is to reduce the impact of data noise by optimizing the model results. Besides, we use post-processing rules to correct three cases of redundant labeling, missing labeling, and wrong labeling in the model prediction results. Our method proposed in this paper achieved strict criteria of 0.9156 and relax criteria of 0.9660 on the final test set, ranking first.

Is Social Media Marketing Really Working?

2013 ◽  
pp. 174-193 ◽  
Author(s):  
Felipe Uribe Saavedra ◽  
Josep Rialp Criado ◽  
Joan Llonch Andreu
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Business Performance ◽  
Dynamic Capabilities ◽  
Social Media Marketing ◽  
Marketing Tool ◽  
Use Of Social Media ◽  
Marketing Intensity ◽  
The Impact

Online social networks have become the fastest growing phenomenon on the Internet and firms are beginning to take advantage of them as a marketing tool. However, the strategic importance of social media marketing is not yet clear, given the novelty and the difficulty of measuring its impact on business performance. This study uses data from 191 Spanish firms from several sectors to measure the impact of the intensity of use of social media marketing on the relationship between the dynamic capabilities of market orientation and entrepreneurial orientation, and business performance. The results provide evidence of the moderating effects of social media marketing intensity on the strength of the mentioned relations and the importance of a strong and committed marketing strategy on digital social networks for businesses.

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