Micro-blogging Social Network Research Articles

Assessing Brazilian protected areas through social media: Insights from 10 years of public interest and engagement.

Social media platforms are a valuable source of data for investigating cultural and political trends related to public interest in nature and conservation. Here, we use the micro-blogging social network Twitter to explore trends in public interest in Brazilian protected areas (PAs). We identified ~400,000 Portuguese language tweets pertaining to all categories of Brazilian PAs over a ten-year period (1 January 2011-31 December 2020). We analysed the content of these tweets and calculated metrics of user engagement (likes and retweets) to uncover patterns and drivers of public interest in Brazilian PAs. Our results indicate that users / tweets mentioning PAs remained stable throughout the sample period. However, engagement with tweets grew steeply, particularly from 2018 onward and coinciding with a change in the Brazilian federal government. Furthermore, public interest was not evenly distributed across PAs; while national parks were the subject of the most tweets, mainly related to tourism activities, tweets related to conflicts among park users and managers were more likely to engage Twitter users. Our study highlights that automatic or semi-automatic monitoring of social media content and engagement has great potential as an early warning system to identify emerging conflicts and to generate data and metrics to support PA policy, governance and management.

From False-Free to Privacy-Oriented Communitarian Microblogging Social Networks

Online Social Networks (OSNs) have gained enormous popularity in recent years. They provide a dynamic platform for sharing content (text messages or multimedia) and for facilitating communication between friends and acquaintances. Microblogging services are a popular form of OSNs. They allow sending small messages in a one-to-many messaging model so that users can communicate with their favorite celebrity, brand, politician, or other regular users without the obligation of a pre-existing social relationship. A chain of privacy-related scandals linked to questionable data handling practices in microblogging services has arisen in the last past few years. Most current microblogging service providers offer centralized services and their business model is based on monitoring, analyzing, and selling users’ activity and patterns. In the end, the personal information shared by the users to benefit from the free-of-charge services is used for the underlying payment in such systems. In this paper, we present Garlanet, a privacy-aware censorship-resistant microblogging social network that does not rely on a centralized service provider as all data is hosted in computers voluntarily contributed by the users of the system. Garlanet provides microblogging functionalities while protecting privacy and preserving the confidentiality and integrity of users and data. It ensures that users’ identities and their social graphs are hidden from the system and adversaries and it provides availability and scalability of the services. We also evaluate the privacy level of Garlanet and we compare it with the privacy level of eight other microblogging systems.

A Novel Deep Learning-based Sentiment Analysis Method Enhanced with Emojis in Microblog Social Networks

ABSTRACT To exactly classify sentiments of microblog reviews with emojis in microblog social networks, this paper first proposes an emoji vectorisation method to achieve emoji vectors. Then, an emoji-text integrated bidirectional LSTM (ET-BiLSTM) model for sentiment analysis is proposed. In this model, review text-based sentence representations are extracted by a bidirectional LSTM network. Emoji-based auxiliary representations are obtained by a new attention mechanism. The two representations are further integrated into final review representation vectors. Finally, experimental results indicate that the proposed ET-BiLSTM model improves the performance of sentiment classification evaluated by macro-P, macro-R and macro-F1 scores in microblog social networks.

Heterogeneous Ensemble with Combined Dimensionality Reduction for Social Spam Detection

This study presents a novel framework based on a heterogeneous ensemble method and a hybrid dimensionality reduction technique for spam detection in micro-blogging social networks. A hybrid of Information Gain (IG) and Principal Component Analysis (PCA) (dimensionality reduction) was implemented for the selection of important features and a heterogeneous ensemble consisting of Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR) and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) classifiers based on Average of Probabilities (AOP) was used for spam detection. The proposed framework was applied on MPI_SWS and SAC’13 Tip spam datasets and the developed models were evaluated based on accuracy, precision, recall, f-measure, and area under the curve (AUC). From the experimental results, the proposed framework (that is, Ensemble + IG + PCA) outperformed other experimented methods on studied spam datasets. Specifically, the proposed method had an average accuracy value of 87.5%, an average precision score of 0.877, an average recall value of 0.845, an average F-measure value of 0.872 and an average AUC value of 0.943. Also, the proposed method had better performance than some existing methods. Consequently, this study has shown that addressing high dimensionality in spam datasets, in this case, a hybrid of IG and PCA with a heterogeneous ensemble method can produce a more effective method for detecting spam contents.

Explainable link prediction based on multi-granularity relation-embedded representation

Existing link prediction methods focus on mining relations of nodes in terms of network structure, ignoring rich attributes of nodes. In the micro-blog social networks, text contents describe users’ diverse behaviors, which depicts ones’ multi-dimensional and multi-granularity preferences. Thus, based on users’ multi-granularity interest, we can predict and explain social relations among users. In this work, we develop an explainable social relation extraction method based on hierarchical semantic specificity matrices of user interest. First, according to users’ micro-blog contents, we model user interest subjects and design their semantic specificity matrices. Then, on the basis of interest subjects, we learn multi-dimensional and multi-granularity semantic specificity matrices with translation mechanism, which reflects user pairs’ follow relation causes from multiple aspects. Finally, by leveraging multi-granularity semantic specificity matrices, we predict and explain users’ social follow relation. To justify our proposal, we conduct extensive experiments on Sina Weibo and Tencent Weibo datasets. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods in Hits, MeanRank and MRR metrics. The proposal can effectively predict users’ follow relation from the aspects of multi-granularity interest subjects, and improve the accuracy of link prediction in social networks.

Nonlinear dynamic information propagation mathematic modeling and analysis based on microblog social network

Based on the complex network theory and topological properties of scale-free network, a new information propagation model (Susceptible Exposed Inactive Infected Recovered, SEIIR) and dynamic evolution equations are proposed based on microblog social network in this paper. Firstly, we analyze the structure of microblog social network through simulating the real microblog social network environment, and the topological properties of the microblog network are researched. Secondly, microblog network structure and topological properties, traditional information propagation models (SIR, SIRS and SEIR) and evolution mechanism are also studied. Dynamic evolution equations of information propagation are constructed in microblog network, and nonlinear dynamic information propagation model is proposed. Effect factors of the information propagation process in microblog network are investigated. Finally, a series of simulation experiments are carried out. Simulation results show that the proposed new SEIIR model and information propagation dynamic evolution equations are reasonable and effective. It reveals the relationship between the state of microblog social network nodes and the evolution public opinion. It accurately depicts the information interaction mechanism and diffusion pattern in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution and information propagation.

Intelligent Analysis of Arabic Tweets for Detection of Suspicious Messages

With the widespread use of messaging via social networks such as Twitter, Instagram, and Facebook, it is becoming imperative for researchers to devise intelligent systems for data analytics in the range of domains like business, health, communication, security, etc. The complex morphological and syntactic structure of Arabic sentences makes them difficult to analyze. This paper presents an intelligent system to analyze Arabic tweets for detecting suspicious messages. We acquired Arabic tweet data from micro-blogging social network Twitter via Twitter Streaming Application Programming Interface and save it in a required file format. The system tokenizes and preprocesses the tweet dataset. Manual labeling is performed on tweet dataset for suspicious (label 1) and not-suspicious (label 0) classes. The labeled tweet dataset is used to train a classifier using supervised machine learning algorithms for the detection of suspicious activities. During the testing phase, the system processes unlabeled tweet data and detects either it belongs to a suspicious or not-suspicious class. We tested the system using six supervised machine learning algorithms: (1) decision tree, (2) k-nearest neighbors, (3) linear discriminant algorithm, (4) support vector machine, (5) artificial neural networks, and (6) long short-term memory networks. A comparative analysis in terms of accuracy, execution time, and confusion matrices of the six classifiers is presented. The execution speed of ANN is lowest. In terms of predicting correct results, the SVM performs best among all the classifiers and yields 86.72% mean accuracy. The major outcomes of this work are development of labeled dataset of Arabic tweets, an intelligent behavior analysis of tweets using six machine learning algorithms to detect suspicious messages, a comparative analysis of six machine learning algorithms, and a development of a statistical benchmark that can be used for future studies about the detection of crimes on social media.

Implicit Community Discovery Based on Microblog Theme Homogeneity

With the rapid development of various social platforms, social networks have accumulated more and more data. These huge and complex data contain valuable information in many fields. It is of far-reaching significance to conduct statistical analysis of these data and dig out valuable information and apply it in real life. Based on the homogeneity of social networks, our paper proposes a framework of discovery algorithms based on the homogeneity of microblogs to realize the theme community discovery of microblog social networks. Firstly, the data set is preprocessed, the invalid data is removed and classified by microblog users. Secondly, the LDA model is used to extract the topic of microblog user blogs to describe the characteristics of microblog users. Thirdly, two users are calculated based on the theme interest. The homogeneity measure between them is used to represent the relationship of social networks. Finally, the community of the implicit social network is realized through unsupervised algorithms to build communities with the same interest as the theme. In order to verify the validity of our proposed algorithm framework, this paper uses data sets from microblog network to conduct experiments. The experimental results show that the algorithm effectively combines the homogeneity measure of the network and has achieved very good results in the implicit community discovery.

Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Microblogs are open and real-time online social network platforms used by people to make posts about their moods, experiences, and interests. It will be very significant to gather microblog users who have similar interests and hobbies into the same community. In this paper, we propose novel approaches for detecting and evolving dynamic microblog communities. First, inspired by the universal gravitation law, we redefine the gravitation relationships among microblog users. Based on the structure of the microblog social network, we define the basic nodes and their gravity tendency and propose the microblog community detection algorithm. Second, we determine the community changes in the microblog social networks at times $t$ and $t+1$ and propose a microblog community evolution algorithm. Third, we define the mutual transformation probability between communities at times $t$ and $t+1$ and propose the microblog community evolution behavior algorithm. The experiment includes a comparison and evaluation of the microblog community detection, evolution, and behavior extraction algorithms and the optimal ranges of the parameters involved in these algorithms. The experimental results indicate that our proposed algorithms have good performance compared to other benchmarking methods.

Improve Manufacturing Industry Sentimental Analysis for Machine Learning

High competitive pressure in the global manufacturing industry makes a successful, successful and sustainable development processes a successful factor. However, analyzes produced by the production control systems are defined by continuous functional improvements defined by large short-rumors. In particular, they do not use data mining to identify hidden patterns in product data. The data mining approach offered by the Advanced Production Analytics Platform is currently based on identification and method based production process optimization. Twitter is a big fast growing micro-blogging social networking platform to express their opinions about the constitution and products game. These ideas are useful for businesses, government and individuals. Therefore, tweets can be used as a valuable source for public opinion. Emotional analysis is a process to automatically identify whether a user-generated speech expresses a positive, negative or neutral point of view of a company.

Information propagation model based on hybrid social factors of opportunity, trust and motivation

The propagation of information in the network is a complex dynamic process. Establishing an accurate information propagation model around common social factors benefits a lot from its evolution simulation for identifying the valuable information and regulating public opinions. In this paper, we propose Opportunity, Social Trust and Game Choice Motivation relying on triadic closure principle in social network and construct a novel information propagation model with respect to combine these three types of social factors. Firstly, the interest similarity between two users is convenient for measuring the opportunity to receive certain information. Secondly, the threshold of social trust is calculated by coupling users’ network influence and content contribution. Thirdly, game choice with a rule to compute the best benefits has recognized as the motivation of users to spread a message. Finally, the game choice information propagation model based on page rank algorithm (GCIP-Page Rank) is proposed. The experimental results show that information propagation led by users experiences such stages as information contact, information trust and information propagation. At the same time, users’ social trust can accelerate the spread of information in microblog social network by considering both the network structure and information content simultaneously.

Complexity of a Microblogging Social Network in the Framework of Modern Nonlinear Science

Recent developments in nonlinear science have caused the formation of a new paradigm called the paradigm of complexity. The self-organized criticality theory constitutes the foundation of this paradigm. To estimate the complexity of a microblogging social network, we used one of the conceptual schemes of the paradigm, namely, the system of key signs of complexity of the external manifestations of the system irrespective of its internal structure. Our research revealed all the key signs of complexity of the time series of a number of microposts. We offer a new model of a microblogging social network as a nonlinear random dynamical system with additive noise in three-dimensional phase space. Implementations of this model in the adiabatic approximation possess all the key signs of complexity, making the model a reasonable evolutionary model for a microblogging social network. The use of adiabatic approximation allows us to model a microblogging social network as a nonlinear random dynamical system with multiplicative noise with the power-law in one-dimensional phase space.

Exploiting social and local contexts propagation for inducing Chinese microblog-specific sentiment lexicons

Sentiment lexicons including opinion words, sentiment phrases, and idioms with sentiment polarities play an important role in sentiment analysis tasks. Apart from explicit sentiment features, extracting implicit sentiment features is a challenging research issue. The sentiment expression is very domain-specific, and constructing a general sentiment lexicon that is suitable for all domains is hard or even impossible. In this paper, we propose a novel sentiment unit context propagation framework to extract Chinese microblog-specific explicit and implicit sentiment features. In the process of the selection of seed sentiment units, we select the seed sentiment units that have a large standard degree of centrality with other units, and mark these units with sentiment labels using general sentiment lexicons and manual calibrations. To realize sentiment label propagation from a small amount of labeled sentiment units to unlabeled ones, we exploit local contexts, topic features, and so`cial relationships among users in microblog social networks. After that, the sentiment scores of units are calculated using unit context sentiment propagation. Experiments on two real-world microblog data sets demonstrate that our method can generate microblog-specific sentiment lexicons effectively. Furthermore, the sentiment classification accuracies significantly outperform state-of-the-art baselines.

Public Opinions Dissemination of Microblog on Complex Network

With the rapid development of Internet and Web2.0 Technology, the fast transmission of information under microblog has become more and more popular with the wide netizens, and the microblog platform has already become a significant place for net citizens to express their opinions. Therefore, the study on the dissemination of public opinions in network is of great significance. In order to explore network public opinions dissemination mechanism of microblog, this paper discusses characteristics of the microblog social network structure and influence they made on information transmitting and sharing in microblog through complex network analysis combined with data obtained from Sina Weibo. This paper presents a mechanism building the microblog social network model, and discusses the scale-free property, small-world effect and centrality property of microblog network through complex network analysis. At last, this paper discusses the public opinions dissemination mechanism in microblog social network with a network event case.

Blogracy

The current approach to build social networking systems is to create huge centralized systems owned by a single company. However, such strategy has many drawbacks, e.g., lack of privacy, lack of anonymity, risks of censorship and operating costs. In this paper the authors propose a novel P2P system that leverages existing, widespread and stable technologies such as DHTs and BitTorrent. In particular, they introduce a key-based identity system and a model of social relations for distributing content efficiently among interested readers. The system they propose, Blogracy, is a micro-blogging social networking system focused on: (i) anonymity and resilience to censorship; (ii) authenticatable content; (iii) semantic interoperability using activity streams. The authors have implemented the system and conducted various experiments to study its behaviour. The results are presented here, regarding (i) communication delays for some simulations of node churn, (ii) delays measured in test operations over PlanetLab, in direct communication, and (iii) through the I2P anonymizing network.

Link Prediction in Microblog Network Using Supervised Learning with Multiple Features

Link prediction (LP) is a fundamental network analysis task. It aims to analyze the existing links and predict the missing or potential relations between users in a social network. It can help users in finding new friends, enhance their loyalties to the web sites and build a healthy social environment. In previous researches, much attention was focused on structure information or node attributes, in order to analyze the global or local properties. Considering the nature of Microblog social network, we proposed a link prediction system combining multiple features from different perspectives, and learn a classifier from these feature subsets to predict the potential links. We train classifiers using SVM, Nai ve Bayes, and Random Forest and Logistic Regression algorithms and evaluate them using the microblog network dataset. The results show that our features perform better than the traditional features, and the combination of multiple features can achieve highest accuracy.

A Point Process Framework for Predicting Popularity of Content in Online Social Networks

In this paper, we extend previous models to predict the popularity of user-generated content for online social networks (OSNs). We focus on Twitter as the most popular and widely used micro-blogging online social network, and specifically measure the popularity of a brand’s tweet by analyzing the time-series path of its subsequent activities (i.e. retweets, replies and marks as favorite) using multivariate marked Hawkes process models. Self and mutually exciting point process models as a multivariate class are adopted to simulate popularity growth patterns of brand post contents on Twitter. While previous self-exciting point process models aggregate the three types of events into one stream of information and therefore ignore exciting effects among different types of events, this study focuses on incorporating the event type into the predictive models for content popularity, explicitly looking at the effects of various types of users’ activities on Twitter. We aim to develop a mutually exciting point process model in order to incorporate such effects among different types of events. Our results suggest that the mutual-exciting point process model outperforms the self-exciting point process model to predict the popularity of online content, as it is able to capture mutual excitement effects and cross interactions between a sequence of events from one type to another. These results are useful in developing and executing a plan for online activities by the brand owners.

Unsupervised topic discovery in micro-blogging networks

Unsupervised automatic topic discovery in micro-blogging social networks is a very challenging task, as it involves the analysis of very short, noisy, ungrammatical and uncontextual messages. Most of the current approaches to this problem are basically syntactic, as they focus either on the use of statistical techniques or on the analysis of the co-occurrences between the terms. This paper presents a novel topic discovery methodology, based on the mapping of hashtags to WordNet terms and their posterior clustering, in which semantics plays a centre role. The paper also presents a detailed case study in the field of Oncology, in which the discovered topics are thoroughly compared to a golden standard, showing promising results.

Measuring User Influence in Sina Microblogging Social Network

As a convenient and popular social network platform, microblogging has become an important medium for people’s information exchange. The development of microblogging network can be regarded as a growth of complex network, and dissemination of information in microblogging network shows strong regularity. In the face of big scale of users, the appraisal of user influence become an important index to measures the value of a user. How to recognize user influence becomes a hot topic in microblogging research. In this paper, we proposed a method named InformationRank to evaluate user influence, which based on measure the information of node betweenness centrality in the network. Then, we analyze and compare the efficiency of this method with PageRank algorithm and Hits algorithm. The experimental results indicate that InformationRank algorithm is effective in a large scale network.

Research and Implement of Micro-Blog Social Network System Based on Users’ Association Analysis

With the widespread application of web2.0 technology, micro-blog has become an important part of life for many people. A well-designed micro-blog system has the better users’ experience and allows users to get the information what they want with high efficiency in the shortest time. After analyzing each of the existing micro-blog systems, we found that the most information of micro-blog is sorted by time. With the users’ attention gradually increased, the frequency of message update, its contents appeared homogeneity and repetition, and thus leaded information overload. To solve this problem, this article puts forward a method based on users’ association analysis and implements a micro-blog system which presents its contents in priority based on users’ interest by way of users’ association analysis. It can sort the contents of the micro-blog and show the contents which the user interest in priority.