DPSN: A Novel approach for Disease Prediction based on Social Networks

2019 ◽  
Author(s):  
Rishabh Gupta ◽  
Swapnil Gupta ◽  
Rahul Gupta ◽  
Neetu Sardana
Keyword(s):  
Social Networks ◽  
Disease Prediction ◽  
Novel Approach

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

scholarly journals An Effective Recommender Algorithm for Cold-Start Problem in Academic Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Vala Ali Rohani ◽  
Zarinah Mohd Kasirun ◽  
Sameer Kumar ◽  
Shahaboddin Shamshirband
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Prediction Accuracy ◽  
Cold Start ◽  
Information Spaces ◽  
Novel Approach ◽  
Significant Performance ◽  
Academic Social Network ◽  
Recommender Algorithm ◽  
Cold Start Problem

Abundance of information in recent years has become a serious challenge for web users. Recommender systems (RSs) have been often utilized to alleviate this issue. RSs prune large information spaces to recommend the most relevant items to users by considering their preferences. Nonetheless, in situations where users or items have few opinions, the recommendations cannot be made properly. This notable shortcoming in practical RSs is called cold-start problem. In the present study, we propose a novel approach to address this problem by incorporating social networking features. Coined as enhanced content-based algorithm using social networking (ECSN), the proposed algorithm considers the submitted ratings of faculty mates and friends besides user’s own preferences. The effectiveness of ECSN algorithm was evaluated by implementing it in MyExpert, a newly designed academic social network (ASN) for academics in Malaysia. Real feedbacks from live interactions of MyExpert users with the recommended items are recorded for 12 consecutive weeks in which four different algorithms, namely, random, collaborative, content-based, and ECSN were applied every three weeks. The empirical results show significant performance of ECSN in mitigating the cold-start problem besides improving the prediction accuracy of recommendations when compared with other studied recommender algorithms.

scholarly journals Private Profile Matching for Mobile Social Networks Based on Fuzzy Extractors

10.29007/st23 ◽  
2018 ◽  
Author(s):  
Jaweher Zouari ◽  
Mohamed Hamdi ◽  
Tai-Hoon Kim
Keyword(s):  
Social Networks ◽  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Mobile Social Networks ◽  
Fuzzy Extractor ◽  
Mobile Social Networking ◽  
Profile Matching ◽  
Novel Approach ◽  
Privacy Breaches ◽  
Fuzzy Extractors

Interacting with geographically proximate users who present similar interests and preferences is a key service offered by mobile social networks which leads to the creation of new connections that combine physical and social closeness. Usually these interactions are based on social profile matching where users publish their preferences and attributes to enable the search for a similar profile. Such public search would result in the leakage of sensitive or identifiable information to strangers who are not always potential friends. As a consequence this promising feature of mobile social networking may cause serious privacy breaches if not addressed properly. Most existent work relies on homomorphic encryption for privacy preservation during profile matching, while we propose in this paper a novel approach based on the fuzzy extractor which performs private matching of two sets and reveals them only if they overlap considerably. Our scheme achieves a desirable trade off between security and complexity.

scholarly journals Fostering Cyber-Physical Social Systems through an Ontological Approach to Personality Classification Based on Social Media Posts

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6611
Author(s):  
Alexandra Cernian ◽  
Nicoleta Vasile ◽  
Ioan Stefan Sacala
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Systems ◽  
Behavior Modeling ◽  
Human Behavior Modeling ◽  
Novel Approach ◽  
Ontological Approach ◽  
Social Media Platforms ◽  
Smart Communities ◽  
Personality Classification

The exponential increase in social networks has led to emergent convergence of cyber-physical systems (CPS) and social computing, accelerating the creation of smart communities and smart organizations and enabling the concept of cyber-physical social systems. Social media platforms have made a significant contribution to what we call human behavior modeling. This paper presents a novel approach to developing a users’ segmentation tool for the Romanian language, based on the four DISC personality types, based on social media statement analysis. We propose and design the ontological modeling approach of the specific vocabulary for each personality and its mapping with text from posts on social networks. This research proposal adds significant value both in terms of scientific and technological contributions (by developing semantic technologies and tools), as well as in terms of business, social and economic impact (by supporting the investigation of smart communities in the context of cyber-physical social systems). For the validation of the model developed we used a dataset of almost 2000 posts retrieved from 10 social medial accounts (Facebook and Twitter) and we have obtained an accuracy of over 90% in identifying the personality profile of the users.

scholarly journals A ML and NLP based Framework for Sentiment Analysis on Bigdata

2020 ◽  
Vol 8 (5) ◽  
pp. 189-200
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Online Social Networks ◽  
Topic Model ◽  
Social Feedback ◽  
Feedback Systems ◽  
Multiple Sources ◽  
Probabilistic Topic Model ◽  
Novel Approach

Big data as multiple sources and social media is one of them. Such data is rich in opinion of people and needs automated approach with Natural Language Processing (NLP) and Machine Learning (ML) to obtain and summarize social feedback. With ML as an integral part of Artificial Intelligence (AI), machines can demonstrate intelligence exhibited by humans. ML is widely used in different domains. With proliferation of Online Social Networks (OSNs), people of all walks of life exchange their views instantly. Thus they became platforms where opinions or people are available. In other words, social feedback on products and services are available. For instance, Twitter produces large volumes of such data which is of much use to enterprises to garner Business Intelligence (BI) useful to make expert decisions. In addition to the traditional feedback systems, the feedback (opinions) over social networks provide depth in the intelligence to revise strategies and policies. Sentiment analysis is the phenomenon which is employed to analyze opinions and classify them into positive, negative and neutral. Existing studies usually treated overall sentiment analysis and aspect-based sentiment analysis in isolation, and then introduce a variety of methods to analyse either overall sentiments or aspect-level sentiments, but not both. Usage of probabilistic topic model is a novel approach in sentiment analysis. In this paper, we proposed a framework for comprehensive analysis of overall and aspect-based sentiments. The framework is realized with aspect based topic modelling for sentiment analysis and ensemble learning algorithms. It also employs many ML algorithms with supervised learning approach. Benchmark datasets used in international SemEval conferences are used for empirical study. Experimental results revealed the efficiency of the proposed framework over the state of the art.

scholarly journals A Novel Approach to the Measurement of Bourdieusian Social Capital within Institutional Pages and Profiles

2022 ◽  
Vol 29 (1) ◽  
pp. 11-27
Author(s):  
Alan Keller Gomes ◽  
Kaique Matheus Rodrigues Cunha ◽  
Guilherme Augusto da Silva Ferreira
Keyword(s):  
Social Capital ◽  
Social Networks ◽  
Social Interactions ◽  
Online Social Networks ◽  
Mutual Recognition ◽  
Pierre Bourdieu ◽  
Sequential Patterns ◽  
Frequency Of Occurrence ◽  
Novel Approach ◽  
The Social

We present in this paper a novel approach for measuring Bourdieusian Social Capital (BSC) within  Institutional Pages and Profiles. We analyse Facebook's Institutional Pages and Twitter's Institutional Profiles. Supported by Pierre Bourdie's theory, we search for directions to identify and capture data related to sociability practices, i. e. actions performed such as Like, Comment and Share. The system of symbolic exchanges and mutual recognition treated by Pierre Bourdieu is represented and extracted automatically from these data in the form of generalized sequential patterns. In this format, the social interactions captured from each page are represented as sequences of actions. Next, we also use such data to measure the frequency of occurrence of each sequence. From such frequencies, we compute the effective mobilization capacity. Finally, the volume of BSC is computed based on the capacity of effective mobilization, the number of social interactions captured and the number of followers on each page. The results are aligned with Bourdieu's theory. The approach can be generalized to institutional pages or profiles in Online Social Networks.

Improving information spread by spreading groups

2019 ◽  
Vol 44 (1) ◽  
pp. 24-42 ◽  
Author(s):  
Alon Sela ◽  
Orit Milo ◽  
Eugene Kagan ◽  
Irad Ben-Gal
Keyword(s):  
Social Networks ◽  
Political Campaigns ◽  
Opinion Leaders ◽  
Group Model ◽  
Information Cascades ◽  
Fake News ◽  
Wall Street ◽  
Content Type ◽  
Novel Approach ◽  
The Wall Street Journal

Purpose The purpose of this paper is to propose a novel method to enhance the spread of messages in social networks by “Spreading Groups.” These sub-structures of highly connected accounts intentionally echo messages between the members of the subgroup at the early stages of a spread. This echoing further boosts the spread to regions substantially larger than the initial region. These spreading accounts can be actual humans or social bots. Design/methodology/approach The paper reveals an interesting anomaly in information cascades in Twitter and proposes the spreading group model that explains this anomaly. The model was tested using an agent-based simulation, real Twitter data and questionnaires. Findings The messages of few anonymous Twitter accounts spread on average more than well-known global financial media groups, such as The Wall Street Journal or Bloomberg. The spreading groups (also sometimes called BotNets) model provides an effective mechanism that can explain these findings. Research limitations/implications Spreading groups are only one possible mechanism that can explain the effectiveness of spread of tweets from lesser known accounts. The implication of this work is in showing how spreading groups can be used as a mechanism to spread messages in social networks. The construction of spreading groups is rather technical and does not require using opinion leaders. Similar to the case of “Fake News,” we expect the topic of spreading groups and their aim to manipulate information to receive growing attention in public discussion. Practical implications While harnessing opinion leaders to spread messages is costly, constructing spreading groups is more technical and replicable. Spreading groups are an efficient method to amplify the spread of message in social networks. Social implications With the blossoming of fake news, one might tend to assess the reliability of news by the number of users involved in its spread. This heuristic might be easily fooled by spreading groups. Furthermore, spreading groups consisting of a blend of human and computerized bots might be hard to detect. They can be used to manipulate financial markets or political campaigns. Originality/value The paper demonstrates an anomaly in Twitter that was not studied before. It proposes a novel approach to spreading messages in social networks. The methods presented in the paper are valuable for anyone interested in spreading messages or an agenda such as political actors or other agenda enthusiasts. While social bots have been widely studied, their synchronization to increase the spread is novel.

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