Appropriate Feature Selection and Post-processing for the Recognition of Artificial Pornographic Images in Social Networks

2016 ◽  
pp. 504-516
Author(s):  
Fangfang Li ◽  
Siwei Luo ◽  
Xiyao Liu ◽  
Jianbin Li
Keyword(s):  
Social Networks ◽  
Feature Selection ◽  
Post Processing

scholarly journals Estratégias para Aprimorar a Diversidade Categórica e Geográfica de Sistemas de Recomendação de POIs

2020 ◽  
Author(s):  
Rodrigo Carvalho ◽  
Leonardo Rocha
Keyword(s):  
Social Networks ◽  
User Satisfaction ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Data Sets ◽  
Processing Strategy ◽  
Post Processing ◽  
Practical Utility ◽  
Points Of Interest ◽  
Location Based Social Networks

Currently, so-called Recommendation Systems (SRs) have been used to assist users in discovering relevant Points of Interest (POIs) on Location-Based Social Networks (LBSN), such as FourSquare and Yelp. Given the main challenges of data-sparse and geographic influence in this scenario, most of the work on POI recommendations has focused only on improving the effectiveness (i.e. accuracy) of the systems. However, there is a growing consensus that just effectiveness is not sufficient to assess the practical utility of these systems. In real scenarios, categorical and geographic diversities were identified as the main complementary dimensions for assessing user satisfaction and the usefulness of recommendations. The works in the literature are concentrated on only one of these concepts. In this work, we propose a new post-processing strategy, which combines these concepts in order to improve the user’s interest in POIs. Our experimental results in the Yelp data sets show that our strategy can improve user satisfaction, considering different SRs and multiple diversification metrics. Our method is capable of improving diversity by up to 120 % without significant losses in terms of effectiveness.

scholarly journals DieTryin: An R package for data collection, automated data entry, and post-processing of network-structured economic games, social networks, and other roster-based dyadic data

2021 ◽  
Author(s):  
Cody T. Ross ◽  
Daniel Redhead
Keyword(s):  
Social Networks ◽  
Data Collection ◽  
Sample Size ◽  
Data Entry ◽  
R Package ◽  
Recall Bias ◽  
Post Processing ◽  
Dyadic Data ◽  
Economic Games ◽  
Respondent Fatigue

AbstractResearchers studying social networks and inter-personal sentiments in bounded or small-scale communities face a trade-off between the use of roster-based and free-recall/name-generator-based survey tools. Roster-based methods scale poorly with sample size, and can more easily lead to respondent fatigue; however, they generally yield higher quality data that are less susceptible to recall bias and that require less post-processing. Name-generator-based methods, in contrast, scale well with sample size and are less likely to lead to respondent fatigue. However, they may be more sensitive to recall bias, and they entail a large amount of highly error-prone post-processing after data collection in order to link elicited names to unique identifiers. Here, we introduce an R package, DieTryin, that allows for roster-based dyadic data to be collected and entered as rapidly as name-generator-based data; DieTryin can be used to run network-structured economic games, as well as collect and process standard social network data and round-robin Likert-scale peer ratings. DieTryin automates photograph standardization, survey tool compilation, and data entry. We present a complete methodological workflow using DieTryin to teach end-users its full functionality.

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