scholarly journals A Study of Rumor Detection based on Social Network Topic Models Relationship

2020 ◽  
Author(s):  
Diogo Nolasco ◽  
Jonice Oliveira
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Community ◽  
Topic Model ◽  
Fake News ◽  
Detection Problem ◽  
Model Method ◽  
Show Evidence ◽  
Rumor Detection ◽  
Scientific Topic

The rumor detection problem on social networks has attracted considerable attention in recent years with the rise of concerns about fake news and disinformation. Most previous works focused on detecting rumors by individual messages, classifying whether a post or blog entry is considered a rumor or not. This paper proposes a method for rumor detection on topic-level that identifies whether a social topic related to a scientific topic is a rumor. We propose the use of a topic model method on social and scientific domains and correlate the topics found to detect the most prone to be rumors. Results applied in the Zika epidemic scenario show evidence that the least correlated topics contain a mix of rumors and local community discussions.

scholarly journals Topical Rumor Detection based on Social Network Topic Models Relationship

2021 ◽  
Vol 14 (2) ◽  
pp. 05-27
Author(s):  
Diogo Nolasco ◽  
Jonice Oliveira
Keyword(s):  
Local Community ◽  
Topic Model ◽  
Fake News ◽  
Reference Set ◽  
The Social ◽  
Show Evidence ◽  
Political Speeches ◽  
Presidential Speeches ◽  
Social Scientific ◽  
Rumor Detection

The rumor detection problem on social networks has attracted considerable attention in recent years with the rise of concerns about fake news and disinformation. Most previous works focused on detecting rumors by individual messages, classifying whether a post or blog entry is considered a rumor or not. This paper proposes a method for rumor detection on topic-level that identifies whether a social topic related to a reference or authoritative topic is a rumor. We propose the use of a topic model method on social, scientific and political domains and correlate the topics found to detect the most prone to be rumors. Two scenarios were analyzed; the Zika epidemic scenario where our reference set of topics are scientific and the Brazilian presidential speeches where our reference set is extracted from the political speeches themselves. Results applied in the Zika epidemic scenario show evidence that the least correlated topics contain a mix of rumors and local community discussions. The Brazilian presidential speeches scenario suggests a strong correlation between rumor topics from both the speeches and the social domains.

scholarly journals Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yanni Liu ◽  
Dongsheng Liu ◽  
Yuwei Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Smart City ◽  
Topic Model ◽  
Rapid Development ◽  
Mobile Internet ◽  
Time Curve ◽  
Public Opinions ◽  
The Social

With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.

Local Community Extraction in Social Network Analysis

2013 ◽  
pp. 172-181
Author(s):  
Xianchao Zhang ◽  
Liang Wang ◽  
Yueting Li ◽  
Wenxin Liang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Local Community ◽  
Linear Time ◽  
Complete Information ◽  
Global Community ◽  
Local Algorithms ◽  
Community Extraction

To identify global community structures in networks is a great challenge that requires complete information of graphs, which is infeasible for some large networks, e.g. large social networks. Recently, local algorithms have been proposed to extract communities for social networks in nearly linear time, which only require a small part of the graphs. In local community extraction, the community extracting assignments are only done for a certain subset of vertices, i.e., identifying one community at a time. Typically, local community detecting techniques randomly start from a vertex and gradually merge neighboring vertices one-at-a-time by optimizing a measure metric. In this chapter, plenty of popular methods are presented that are designed to obtain a local community for a given graph.

scholarly journals A word embedding topic model for topic detection and summary in social networks

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1289-1298 ◽  
Author(s):  
Lei Shi ◽  
Gang Cheng ◽  
Shang-ru Xie ◽  
Gang Xie
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Topic Model ◽  
Word Embedding ◽  
Probabilistic Latent Semantic Analysis ◽  
Topic Detection ◽  
Short Text ◽  
Internal Relationship

The aim of topic detection is to automatically identify the events and hot topics in social networks and continuously track known topics. Applying the traditional methods such as Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis is difficult given the high dimensionality of massive event texts and the short-text sparsity problems of social networks. The problem also exists of unclear topics caused by the sparse distribution of topics. To solve the above challenge, we propose a novel word embedding topic model by combining the topic model and the continuous bag-of-words mode (Cbow) method in word embedding method, named Cbow Topic Model (CTM), for topic detection and summary in social networks. We conduct similar word clustering of the target social network text dataset by introducing the classic Cbow word vectorization method, which can effectively learn the internal relationship between words and reduce the dimensionality of the input texts. We employ the topic model-to-model short text for effectively weakening the sparsity problem of social network texts. To detect and summarize the topic, we propose a topic detection method by leveraging similarity computing for social networks. We collected a Sina microblog dataset to conduct various experiments. The experimental results demonstrate that the CTM method is superior to the existing topic model method.

An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm

2017 ◽  
pp. 1062-1075 ◽  
Author(s):  
Khaled Ahmed ◽  
Aboul Ella Hassanien ◽  
Ehab Ezzat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Label Propagation ◽  
Optimization Approach ◽  
Detection Problem ◽  
Swarm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

Complex social networks analysis is an important research trend, which basically based on community detection. Community detection is the process of dividing the complex social network into a dynamic number of clusters based on their edges connectivity. This paper presents an efficient Elephant Swarm Optimization Algorithm for community detection problem (EESO) as an optimization approach. EESO can define dynamically the number of communities within complex social network. Experimental results are proved that EESO can handle the community detection problem and define the structure of complex networks with high accuracy and quality measures of NMI and modularity over four popular benchmarks such as Zachary Karate Club, Bottlenose Dolphin, American college football and Facebook. EESO presents high promised results against eight community detection algorithms such as discrete krill herd algorithm, discrete Bat algorithm, artificial fish swarm algorithm, fast greedy, label propagation, walktrap, Multilevel and InfoMap.

Social Network Analysis Based on Topic Model with Temporal Factor

2018 ◽  
Vol 9 (1) ◽  
pp. 82-97 ◽  
Author(s):  
Thanh Ho ◽  
Phuc Do
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Topic Model ◽  
Traditional Approach ◽  
Model Based ◽  
Factor Ii ◽  
Latent Topics ◽  
Topic Taxonomy ◽  
Temporal Factor

On social networks, each message has many features where the interested topics and the actors sending and receiving topics are important features. Unlike the traditional approach, which views each message belonging to a topic, the topic model is based on the approach, which indicates that each message has a mixture of many topics. However, topic model has limitations about discovering interested topics of actors with temporal factor and labelling latent topics. The article proposes a temporal-author-recipient-topic (TART) model based on: (i) discovering interested topics and analyzing the role of actors on social networks with the temporal factor; (ii) labelling the latent topics from topic model based on topic taxonomy; (iii) applying the temporal factor for finding the relation among factors in model; and (iv) finding out the variation of interested topics of actors with each period of time. An experimenting TART model on two corpora with 1,004,396 messages in Vietnamese and 25,009 actors by the software is built for SNA.

scholarly journals A trust model for spreading gossip in social networks: a multi-type bootstrap percolation model

2020 ◽  
Vol 476 (2235) ◽  
pp. 20190826 ◽  
Author(s):  
Rinni Bhansali ◽  
Laura P. Schaposnik
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Trust Model ◽  
Percolation Model ◽  
Marketing Strategies ◽  
Bootstrap Percolation ◽  
Information Propagation ◽  
Fake News ◽  
Minimum Threshold ◽  
Bp Model

We introduce here a multi-type bootstrap percolation model, which we call T -Bootstrap Percolation ( T -BP), and apply it to study information propagation in social networks. In this model, a social network is represented by a graph G whose vertices have different labels corresponding to the type of role the person plays in the network (e.g. a student, an educator etc.). Once an initial set of vertices of G is randomly selected to be carrying a gossip (e.g. to be infected), the gossip propagates to a new vertex provided it is transmitted by a minimum threshold of vertices with different labels. By considering random graphs, which have been shown to closely represent social networks, we study different properties of the T -BP model through numerical simulations, and describe its implications when applied to rumour spread, fake news and marketing strategies.

scholarly journals Consumers Team Detection Model Based on Trust for Multi-Level

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Xiaoming Li ◽  
Guangquan Xu ◽  
Sandhya Armoogum ◽  
Honghao Gao
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Targeted Delivery ◽  
Large Scale ◽  
Information Dissemination ◽  
Local Community ◽  
Detection Methods ◽  
Detection Model ◽  
User Behaviors ◽  
Marketing Data

Due to rapid advances in technology, social networks have become important platforms for daily communication, product marketing, and information dissemination. Targeted delivery of social network advertisement can considerably improve the efficacy of the advertisement and maximize the profits from it. In this context, managing the specific audience of a social network advertisement and achieving targeted advertisement delivery have been the ultimate goals of the social network advertising sector. Identifying user groups with similar properties is critical to increasing targeted sales. When both the scale of mobile social network and the coplexity of social network user behaviors grow, similar groups are hidden in user behaviors. In order to analyze community structure with user trust relationship more appropriately in the large-scale multilevel social network environment, a novel local community detection model E-MLCD is proposed in this paper. It is jointly based on the multilevel properties and the strength of similarity of multilevel social interaction among communities. By studying three real-world multilevel social networks and specific QQ Zone marketing data, the model defines a new metric of community trust based on similarity. Comparison between other state-of-the-art detection methods demonstrate E-MLCD’s ability to detect communities more effectively.

The use of social networks in marketing: the Italian co-operative experience

2015 ◽  
Vol 7 (1) ◽  
pp. 31-57 ◽  
Author(s):  
Patrizia Battilani ◽  
Giuliana Bertagnoni
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Marketing Strategy ◽  
Local Community ◽  
National Level ◽  
Viral Marketing ◽  
Original Research ◽  
Content Type ◽  
The Social

Purpose – The main aim of our study is to demonstrate that the Italian way to marketing included not only the “advertising artists” but also what can be labelled as the social network approach, which was mainly used by cooperative enterprises. Focussing on the case study of the Granarolo co-operative, the paper discusses the social network method of marketing as it emerged during the 1950s and 1960s in Italy. Design/methodology/approach – The research draws on different types of primary sources, including co-operative business records, interviews, publications, newspaper articles and advertisements. Findings – In the age of mass consumption, the Granarolo co-operative developed an original marketing strategy based on social networks. This strategy can be considered a kind of community brand based on shared values pre-existing to the brand itself and a kind of viral marketing put in place before the electronic revolution. Research limitations/implications – The research focusses on the Granarolo case study. It can be extended to other co-operative enterprises. However, it is unknown whether the anticipation of viral marketing has also been used by private enterprises. Practical implications – The marketing strategies analyzed in the paper could be a interesting solution for undertakings strictly connected and rooted in their local community or in their Web community. Social implications – In today’s world of the Web, this physical constraint no longer exists, and the social method of marketing exceeds the regional and even the national level. In conclusion, this was an innovative method of marketing and advertising that came into being, ahead of its time, about a half a century before modern Web-based social networks were conceived, yet uses the same concepts, hence its extraordinary originality. Originality/value – This study is the result of an original research which tries to highlight what we could label the Italian way to marketing. Taking into consideration the first two decades of the Granarolo history and focussing on the marketing strategy, our contribution seeks to examine how the social networks approach worked and in what it differs from today brand community and viral marketing.

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