Showing papers on "Microblogging published in 2019"
TL;DR: This article proposed a CLMB model for analysing microblogging user behaviour and their activity across different countries in the CPSS applications, and evaluated CLBM model under the collected microblog dataset from 16 countries with the largest number of representative and active users in the world.
Abstract: As the rapid growth of social media technologies continues, Cyber-Physical-Social System (CPSS) has been a hot topic in many industrial applications. The use of “microblogging” services, such as Twitter, has rapidly become an influential way to share information. While recent studies have revealed that understanding and modelling microblog user behaviour with massive users’ data in social media are keen to success of many practical applications in CPSS, a key challenge in literatures is that diversity of geography and cultures in social media technologies strongly affect user behaviour and activity. The motivation of this article is to understand differences and similarities between microblogging users from different countries using social media technologies, and to attempt to design a Country-Level Micro-Blog User (CLMB) behaviour and activity model for supporting CPSS applications. We proposed a CLMB model for analysing microblogging user behaviour and their activity across different countries in the CPSS applications. The model has considered three important characteristics of user behaviour in microblogging data, including content of microblogging messages, user emotion index, and user relationship network. We evaluated CLBM model under the collected microblog dataset from 16 countries with the largest number of representative and active users in the world. Experimental results show that (1) for some countries with small population and strong cohesiveness, users pay more attention to social functionalities of microblogging service; (2) for some countries containing mostly large loose social groups, users use microblogging services as a news dissemination platform; (3) users in countries whose social network structure exhibits reciprocity rather than hierarchy will use more linguistic elements to express happiness in microblogging services.
207 citations
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TL;DR: There has been a wide array of domains ranging from fast-moving consumer products to political events where sentiment analysis has numerous applications and these innumerable applications and interests have been the driving source towards sentiment analysis research.
Abstract: There has been a wide array of domains ranging from fast-moving consumer products to political events where sentiment analysis has numerous applications. Several large companies have their own in-built capabilities in this area. These innumerable applications and interests have been the driving source towards sentiment analysis research. Several social networks and microblogs have provided strong platforms for users’ information exchange and communication. The social networks and microblogs provide trillions of pieces of multimodal information.
154 citations
TL;DR: The result shows most of the articles applied opinion-lexicon method to analyses text sentiment in social media, extracted data on microblogging site mainly Twitter and sentiment analysis application can be seen in world events, healthcare, politics and business.
146 citations
TL;DR: Twitter is an enormously popular microblog on which clients may voice their opinions and opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting “tweets” (comments) and the content of these expressions.
Abstract: The entire world is transforming quickly under the present innovations. The Internet has become a basic requirement for everybody with the Web being utilized in every field. With the rapid increase in social network applications, people are using these platforms to voice them their opinions with regard to daily issues. Gathering and analyzing peoples’ reactions toward buying a product, public services, and so on are vital. Sentiment analysis (or opinion mining) is a common dialogue preparing task that aims to discover the sentiments behind opinions in texts on varying subjects. In recent years, researchers in the field of sentiment analysis have been concerned with analyzing opinions on different topics such as movies, commercial products, and daily societal issues. Twitter is an enormously popular microblog on which clients may voice their opinions. Opinion investigation of Twitter data is a field that has been given much attention over the last decade and involves dissecting “tweets” (comments) and the content of these expressions. As such, this paper explores the various sentiment analysis applied to Twitter data and their outcomes.
136 citations
TL;DR: This work modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level.
Abstract: Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is...
101 citations
TL;DR: The main aim of the work is to process the raw sentence from the Twitter dataset and find the actual polarity of the message and the proposed model performs well in normalization and sentiment analysis of the raw Twitter data enriched with hidden information.
Abstract: On social media platforms such as Twitter and Facebook, people express their views, arguments, and emotions of many events in daily life. Twitter is an international microblogging service featuring short messages called “tweets” from different languages. These texts often consist of noise in the form of incorrect grammar, abbreviations, freestyle, and typographical errors. Sentiment analysis (SA) aims to predict the actual emotions from the raw text expressed by the people through the field of natural language processing (NLP). The main aim of our work is to process the raw sentence from the Twitter dataset and find the actual polarity of the message. This paper proposes a text normalization with deep convolutional character level embedding (Conv-char-Emb) neural network model for SA of unstructured data. This model can tackle the problems: (1) processing the noisy sentence for sentiment detection (2) handling small memory space in word level embedded learning (3) accurate sentiment analysis of the unstructured data. The initial preprocessing stage for performing text normalization includes the following steps: tokenization, out of vocabulary (OOV) detection and its replacement, lemmatization and stemming. A character-based embedding in convolutional neural network (CNN) is an effective and efficient technique for SA that uses less learnable parameters in feature representation. Thus, the proposed method performs both the normalization and classification of sentiments for unstructured sentences. The experimental results are evaluated in the Twitter dataset by a different point polarity (positive, negative and neutral). As a result, our model performs well in normalization and sentiment analysis of the raw Twitter data enriched with hidden information.
73 citations
TL;DR: It is found that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban!
Abstract: How to improve the predictive accuracy of box office revenue with social media data is a big challenge and is particularly important for movie distributors and cinema operators. In this research, we find that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban! Movies (DUGC) based on our examination of 60 movies released in China in 2012. To increase the attendance rate of movies, cinema operators can consider previous valence and volume of MUGC before scheduling the current film screenings because these messages can quickly predict the future box office revenue of a movie. Besides, we find that the volume of enterprise microblogs (i.e., MGC) can predict both box office revenue and MUGC, indicating that movie distributors should optimize their online media strategy by shifting more resources to utilizing enterprise microblogging. Although rebroadcasting volume from microblogging platforms does not predict box office revenue directl...
65 citations
TL;DR: In this article, the authors investigated the presence and impact of fake stock microblogs on the stock market and found that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot-detection algorithm.
Abstract: Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in financial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never been systematically investigated before. Here, we study 9M tweets related to stocks of the five main financial markets in the US. By comparing tweets with financial data from Google Finance, we highlight important characteristics of Twitter stock microblogs. More importantly, we uncover a malicious practice—referred to as cashtag piggybacking—perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the findings of our study is that as much as 71% of the authors of suspicious financial tweets are classified as bots by a state-of-the-art spambot-detection algorithm. Furthermore, 37% of them were suspended by Twitter a few months after our investigation. Our results call for the adoption of spam- and bot-detection techniques in all studies and applications that exploit user-generated content for predicting the stock market.
64 citations
TL;DR: This work will attempt to provide an overall review of the credibility assessment literature over the period 2006–2017 as applied to the context of the microblogging platform, Twitter.
Abstract: The importance of information credibility in society cannot be underestimated given that it is at the heart of all decision-making. Generally, more information is better; however, knowing the value of this information is essential for the decision-making processes. Information credibility defines a measure of the fitness of the information for consumption. It can also be defined in terms of reliability, which denotes the probability that a data source will appear credible to the users. A challenge in this topic is that there is a great deal of literature that has developed different credibility dimensions. In addition, information science dealing with online social networks has grown in complexity, attracting interest from researchers in information science, psychology, human–computer interaction, communication studies, and management studies, all of whom have studied the topic from different perspectives. This work will attempt to provide an overall review of the credibility assessment literature over the period 2006–2017 as applied to the context of the microblogging platform, Twitter. The known interpretations of credibility will be examined, particularly as they relate to the Twitter environment. In addition, we investigate levels of credibility assessment features. We then discuss recent works, addressing a new taxonomy of credibility analysis and assessment techniques. At last, a cross-referencing of literature is performed while suggesting new topics for future studies of credibility assessment in a social media context.
61 citations
TL;DR: A hybrid approach which leverages the capabilities of machine learning techniques to identify malicious profiles on the Twitter microblogging platform and achieves a high detection rate which is better than other techniques.
58 citations
TL;DR: This paper describes how Twitter data are extracted, and the sentiment of the tweets on a particular topic is calculated, and finds that the Word2vec feature extraction method combined with a stack of the CNN and LSTM algorithms achieved the highest accuracy.
Abstract: Twitter is a leading platform among social media networks. It allows microblogging of up to 140 characters for a single post. Owing to this characteristic, it is popular among users. People tweet about various topics from daily life events to major incidents. Given the influence of this social media platform, the analysis of Twitter contents has become a research area as it gives us useful insights on a topic. Hence, this paper will describe how Twitter data are extracted, and the sentiment of the tweets on a particular topic is calculated. This paper focusses on tweets of two halal products, i.e., halal tourism and halal cosmetics. Twitter data (over a 10-year span) were extracted using the Twitter search function, and an algorithm was used to filter the data. Then, an experiment was conducted to calculate and analyze the tweets' sentiment using deep learning algorithms. In addition, convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN) were utilized to improve the accuracy and construct prediction models. Among the results, it was found that the Word2vec feature extraction method combined with a stack of the CNN and LSTM algorithms achieved the highest accuracy of 93.78%.
TL;DR: It is argued that for LGBTQ users, Tumblr simultaneously generates the specter of a “queer utopia’—a space where queer potential flourishes and more expansive ways to think about the future materialize—and queer “vortextuality”—an experience of being sucked into an online black hole with severe limitations.
Abstract: Drawing on one year of fieldwork with LGBTQ youth, I explore the ways Tumblr, a microblogging and social networking site, has become a vibrant center of queer discourse and collectivity. I argue th...
TL;DR: A systematic literature review for rumor detection using deep neural network approaches and presents the challenges and issues that are faced by the researchers in this area and suggests promising future research directions.
Abstract: With the rapid increase in the popularity of social networks, the propagation of rumors is also increasing. Rumors can spread among thousands of users immediately without verification and can cause serious damages. Recently, several research studies have been investigated to control online rumors automatically by mining rich text available on the open network with deep learning techniques. In this paper, we conducted a systematic literature review for rumor detection using deep neural network approaches. A total of 108 studies were retrieved using manual research from five databases (IEEE Explore, Springer Link, Science Direct, ACM Digital Library, and Google Scholar). The considered studies are then examined in our systematic review to answer the seven research questions that we have formulated to deeply understand the overall trends in the use of deep learning methods for rumor detection. Apart from this, our systematic review also presents the challenges and issues that are faced by the researchers in this area and suggests promising future research directions. Our review will be beneficial for researchers in this domain as it will facilitate researchers' comparison with the existing works due to the availability of a complete description of the used performance matrices, dataset characteristics, and the deep learning model used per each work. Our review will also assist researchers in finding the available annotated datasets that can be used as benchmarks for comparing their new proposed approaches with the existing state-of-the-art works.
TL;DR: A hierarchical taxonomy is presented and three lines of thoughts are uncovered: the feature-based approach, time-series modelling, and the collaborative filtering approach and analyse them, respectively, to establish a systematic overview of popularity prediction methods for microblog.
Abstract: As social networks become a major source of information, predicting the outcome of information diffusion has appeared intriguing to both researchers and practitioners. By organizing and categorizing the joint efforts of numerous studies on popularity prediction, this article presents a hierarchical taxonomy and helps to establish a systematic overview of popularity prediction methods for microblog. Specifically, we uncover three lines of thoughts: the feature-based approach, time-series modelling, and the collaborative filtering approach and analyse them, respectively. Furthermore, we also categorize prediction methods based on their underlying rationale: whether they attempt to model the motivation of users or monitor the early responses. Finally, we put these prediction methods to test by performing experiments on real-life data collected from popular social networks Twitter and Weibo. We compare the methods in terms of accuracy, efficiency, timeliness, robustness, and bias.As far as we are concerned, there is no precedented survey aimed at microblog popularity prediction at the time of submission. By establishing a taxonomy and evaluation for the first time, we hope to provide an in-depth review of state-of-the-art prediction methods and point out directions for further research. Our evaluations show that time-series modelling has the advantage of high accuracy and the ability to improve over time. The feature-based methods using only temporal features performs nearly as well as using all possible features, producing average results. This suggests that temporal features do have strong predictive power and that power is better exploited with time-series models. On the other hand, this implies that we know little about the future popularity of an item before it is posted, which may be the focus of further research.
TL;DR: It is shown that expert sentiment signals can yield higher risk-adjusted returns than classical price-based signals and is sufficient to devise hypothetically profitable cross-sectional as well as time series momentum investment strategies for futures based on Twitter signals that survive basic transaction cost assumptions.
Abstract: We examine the long-term relationship between signals derived from nine years of unstructured social media microblog text data and financial market developments in five major economic regions. Employing statistical language modeling techniques we construct directional sentiment metrics and link these to aggregate stock index returns. To address the noise in finance-related Twitter messages we identify expert users whose tweets predominantly focus on finance topics. We document that expert users are the main drivers behind an interdependence between Twitter sentiment and financial markets. The direct prediction value of expert sentiment metrics for stock index returns, however, is found to be elusive and short-lived. Yet, we detect significant predictive gains over benchmark models in times of negative market returns. In consequence, the relation between expert sentiment metrics and stock indices is sufficient to devise hypothetically profitable cross-sectional as well as time series momentum investment strategies for futures based on Twitter signals that survive basic transaction cost assumptions. In this context, our results show that expert sentiment signals can yield higher risk-adjusted returns than classical price-based signals.
TL;DR: Twitter can be a quick and easy-to-use tool to increase exposure to evidence-based information from academic journals in plastic surgery, and five of the six journals with Twitter profiles experienced increases in their impact factor since joining Twitter.
Abstract: Background:Social media have revolutionized the way we access information. Twitter is the most popular microblogging website and has become a tool for plastic surgery journals to connect with the greater academic community and public. The purpose of this study was to objectively assess the use of Tw
27 Jul 2019
TL;DR: This research paper will focus on techniques of sentiment analysis where it will perform how to extract tweets from twitter and eventually it will compare different sentiment analysis techniques and also the approaches containing twitter dataset.
Abstract: Twitter is the popular micro blogging site where thousands of people exchange their thoughts daily in the form of tweets. The characteristics of tweet is to be short and simple way of expressions. Though this paper will focus on sentiment analysis of twitter data. The research area of sentiment analysis are text data mining and NLP. In different form we can perform the sentiment analysis on twitter data. This research paper will focus on techniques of sentiment analysis where we will perform how to extract tweets from twitter. Eventually we will compare different sentiment analysis techniques and also the approaches containing twitter dataset.
01 Jan 2019
TL;DR: This manuscript summarizes the data set of Twitter messages related to recent 14th Gujarat Legislative Assembly Election, 2017, for predicting the chances of winning party by utilizing public’s opinion using NRC Emotion Lexicon to determine the overall tone of the event by eight emotions.
Abstract: Today microblogging has become a very common platform for exchanging opinion among us. Many users exchange their thoughts on various aspects of their activity. Consequently, microblogging Web sites are the substantial origin of information for sentiment analysis and opinion mining. Twitter is a famous microblogging Web site where 500 million tweets are posted every day. In this manuscript, we summarize the data set of Twitter messages related to recent 14th Gujarat Legislative Assembly Election, 2017, for predicting the chances of winning party by utilizing public’s opinion. We use NRC Emotion Lexicon to determine the overall tone of the event by eight emotions. Furthermore, we use a deep learning tool named ParallelDots AI APIs by ParallelDots, Inc. that can analyze the sentiment into positive, negative, and neutral. This tool helped to extract various people’s sentiment and summarize the results for further decision making.
01 May 2019
TL;DR: This work is the first effort to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words, and proposes to jointly model the target posts and the conversation contexts initiated by them with bidirectional attention.
Abstract: Automatic hashtag annotation plays an important role in content understanding for microblog posts. To date, progress made in this field has been restricted to phrase selection from limited candidates, or word-level hashtag discovery using topic models. Different from previous work considering hashtags to be inseparable, our work is the first effort to annotate hashtags with a novel sequence generation framework via viewing the hashtag as a short sequence of words. Moreover, to address the data sparsity issue in processing short microblog posts, we propose to jointly model the target posts and the conversation contexts initiated by them with bidirectional attention. Extensive experimental results on two large-scale datasets, newly collected from English Twitter and Chinese Weibo, show that our model significantly outperforms state-of-the-art models based on classification. Further studies demonstrate our ability to effectively generate rare and even unseen hashtags, which is however not possible for most existing methods.
TL;DR: Wang et al. as mentioned in this paper explored whether social media users' motivations differed when following a sport organization on two similar microblogs: Twitter and Weibo, and found that Weibo users had higher motives for obtaining information, entertainment, technical knowledge, passing time, and escaping from their life than Twitter users.
TL;DR: A new social media based event summarization framework is put forward, which comprises of three stages: a coarse-to-fine filtering model is exploited to eliminate irrelevant information, and a novel User–Text–Image Co-clustering (UTICC) is proposed to jointly discover subevents from microblogs of multiple media types—user, text, and image.
Abstract: Microblogging services have changed the way that people exchange information. There will generate a large number of data on the web once popular events or emergencies occur, including textual descriptions about the time, location and details for the event. Meanwhile users can review, comment, spread the event conveniently. It has always been a hot issue that how to use this mass of data to detect and predict breaking events. While existing approaches mostly only focus on event detection, event location estimation and text-based summary, a small amount of works have focused on event summarization. In this paper, we put forward a new social media based event summarization framework, which comprises of three stages: (1) A coarse-to-fine filtering model is exploited to eliminate irrelevant information. (2) A novel User–Text–Image Co-clustering (UTICC) is proposed to jointly discover subevents from microblogs of multiple media types—user, text, and image. (3) A multimedia event summarization process is designed to identify both representative texts and images, which are further aggregated to form a holistic visualized summary for the events. We conduct extensive experiments on Weibo dataset to demonstrate the superiority of the proposed framework compared to the state-of-the-art approaches.
TL;DR: A novel approach based on graph analysis which will use community structure detection algorithm to detect topics in the keywords graph of micro-blogging data to meet the dual requirements of topic detection and community detection is proposed.
Abstract: In this paper, we will propose a novel approach based on graph analysis which will use community structure detection algorithm to detect topics in the keywords graph of micro-blogging data. Furthermore, considering the specificity of the Sina microblogging, we propose novel keywords filtering model and graph generation algorithm to meet the dual requirements of topic detection and community detection. We validate our approach on a big natural disaster dataset from Sina micro-blog, in which about 103 micro-blogging posts with about 104 distinct feature tags. The experimental results definitely revealed the relationship between the keywords and the natural disaster topics. Our methodology is a scalable method which can adapt to the changes in the amount of data. Especially, we can get abundant information about natural disasters in the topic detection and help the government guide the rescue of disasters.
TL;DR: In this article, a bibliometric analysis was conducted in order to identify significant authors, journals, and institutions who engaged in the research oriented towards Twitter utilization in tourism, and text-mining analysis has been conducted to extract and identify the topics of the papers investigating the utilization of Twitter for tourism research.
Abstract: Background: Twitter is the most popular microblog platform. Individuals, companies, organizations, and even governments use Twitter on a daily bases and get vast benefits from it. Twitter also has been valuable for the tourism sector, especially in developing business strategies, planning and studying tourist decision-making processes. Objectives: Goal of the paper is to identify the trends, patterns and the research gaps of the research focusing on the Twitter usage in tourism. Methods/Approach: A bibliometric analysis was conducted in order to identify significant authors, journals, and institutions who engaged in the research-oriented towards Twitter utilization in tourism. In addition, text-mining analysis has been conducted in order to extract and identify the topics of the papers investigating the utilization of Twitter for tourism research. Results: Research of Twitter utilization in tourism has increased substantially in the last decade, with most of the research conducted in the United States and Japan. Extracted topics are focused on distinctive themes, such as network analysis, word of mouth, and destination management. Conclusions: New topics have emerged, such as the utilization of Twitter in crisis communication and terrorist attacks, as well as the integration of Twitter and other social media such as Flickr.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
TL;DR: This article reviews the state of the art of OSNs existing either in the literature or deployed for use and analyzes and evaluates each system based on a set of characteristics, and compares them based on their usability and the level of protection of privacy and security they provide.
Abstract: Online Social Networks (OSNs) incorporate different forms of interactive communication, including microblogging services, multimedia sharing, business networking, and so on. They allow users to create profiles, connect with friends, and share their daily activities and thoughts. However, this ease of use of OSNs comes with a cost in terms of users’ privacy and security. The big amount of personal data shared in the users’ profiles or correlated from their activities can be stored, processed, and sold for advertisement or statistical purposes. It attracts also malicious users who can collect and exploit the data and target different types of attacks. In this article, we review the state of the art of OSNs existing either in the literature or deployed for use. We focus on the OSN systems that offer, but not exclusively, microblogging services. We analyze and evaluate each system based on a set of characteristics, and we compare them based on their usability and the level of protection of privacy and security they provide. This study is a first step toward understanding the security and privacy controls and measuring their level in an OSN.
TL;DR: Twitter, being the most popular microblogging site, is used to collect the data to perform analysis and Python language is used in this research to implement the classification algorithm on the collected data.
Abstract: Any opinion of an individual through which the feelings, attitudes and thoughts can be expressed is known as sentiment. The kinds of data analysis which is attained from the news reports, user reviews, social media updates or microblogging sites is called sentiment analysis which is also known as opinion mining. The reviews of individuals towards certain events, brands, product or company can be known through sentiment analysis. The responses of general public are collected and improvised by researchers to perform evaluations.
The popularity of sentiment analysis is growing today since the numbers of views being shared by people on the microblogging sites are also increasing. All the sentiments can be categorized into three different categories called positive, negative and neutral. Twitter, being the most popular microblogging site, is used to collect the data to perform analysis. Tweepy is used to extract the source data from Twitter. Python language is used in this research to implement the classification algorithm on the collected data.
The features are extracted using N-gram modeling technique. The sentiments are categorized among positive, negative and neutral using a supervised machine learning algorithm known as K-Nearest Neighbor.
TL;DR: Using evolutionary search algorithm, a compact model for spam account detection is proposed, which is incorporated in the machine learning phase of the unified framework, and indicates that the proposed framework is promising for detecting both spam message and spam account with a minimal number of features.
Abstract: Short message communication media, such as mobile and microblogging social networks, have become attractive platforms for spammers to disseminate unsolicited contents. However, the traditional content-based methods for spam detection degraded in performance due to many factors. For instance, unlike the contents posted on social networks like Facebook and Renren, SMS and microblogging messages have limited size with the presence of many domain specific words, such as idioms and abbreviations. In addition, microblogging messages are very unstructured and noisy. These distinguished characteristics posed challenges to existing email spam detection models for effective spam identification in short message communication media. The state-of-the-art solutions for social spam accounts detection have faced different evasion tactics in the hands of intelligent spammers. In this paper, a unified framework is proposed for both spam message and spam account detection tasks. We utilized four datasets in this study, two of which are from SMS spam message domain and the remaining two from Twitter microblog. To identify a minimal number of features for spam account detection on Twitter, this paper studied bio-inspired evolutionary search method. Using evolutionary search algorithm, a compact model for spam account detection is proposed, which is incorporated in the machine learning phase of the unified framework. The results of the various experiments conducted indicate that the proposed framework is promising for detecting both spam message and spam account with a minimal number of features.
TL;DR: This paper proposes and implements an effective time+user dual attention mechanism model and combines the textual information with sentiment time series to achieve multi-document sentiment prediction on public opinion texts about some specific events on social network platforms.
Abstract: In today’s information age, the development of hot events is timely and rapid under the influence of the powerful Internet. Online social media, such as Weibo in China, has played an important role in the process of spreading public opinions and events. Sentiment analysis of social network texts can effectively reflect the development and changes of public opinions. At the same time, prediction and judgment of public opinion development can also play a key role in assisting decision-making and effective management. Therefore, sentiment analysis for hot events in online social media texts and judgment of public opinion development have become popular topics in recent years. At present, research on textual sentiment analysis is mainly aimed at a single text, and there is little-integrated analysis of multi-user and multi-document in unit time for time series. Moreover, most of the existing methods are focused on the information mined from the text itself, while the feature of identity differences and time sequence of different users and texts on social platforms are rarely studied. Hence, this paper works on the public opinion texts about some specific events on social network platforms and combines the textual information with sentiment time series to achieve multi-document sentiment prediction. Considering the related features of different social user identities and time series, we propose and implement an effective time+user dual attention mechanism model to analyze and predict the textual information of public opinion. The effectiveness of the proposed model is then verified through experiments on real data from a popular Chinese microblog platform called Sina Weibo.
TL;DR: Wang et al. as mentioned in this paper examined how microblogging by top executives affects the information environment for listed firms in an emerging market and found that a board chair having a Weibo account is associated with the dissemination of more firm-specific information to the capital market.
TL;DR: This paper proposes a model for efficient evolutionary user interest community discovery which employs a nature-inspired genetic algorithm to improve the quality of community discovery and validates the effectiveness of the proposed model on the real datasets.
Abstract: Internet of People (IoP), which focuses on personal information collection by a wide range of the mobile applications, is the next frontier for Internet of Things. Nowadays, people become more and more dependent on the Internet, increasingly receiving and sending information on social networks (e.g., Twitter, etc.); thus social networks play a decisive role in IoP. Therefore, community discovery has emerged as one of the most challenging problems in social networks analysis. To this end, many algorithms have been proposed to detect communities in static networks. However, microblogging social networks are extremely dynamic in both content distribution and topological structure. In this paper, we propose a model for efficient evolutionary user interest community discovery which employs a nature-inspired genetic algorithm to improve the quality of community discovery. Specifically, a preprocessing method based on hypertext induced topic search improves the quality of initial users and posts, and a label propagation method is used to restrict the conditions of the mutation process to further improve the efficiency and effectiveness of user interest community detection. Finally, the experiments on the real datasets validate the effectiveness of the proposed model.
01 Jan 2019
TL;DR: This work evaluates and compares the performance of 8 extractive summarization algorithms in the application of summarizing microblogs posted during emergency events, and finds significant differences among the summaries produced by different algorithms over the same input data.
Abstract: Microblogging sites, notably Twitter, have become important sources of real-time situational information during emergency events. Since hundreds to thousands of microblogs (tweets) are generally posted on Twitter during an emergency event, manually going through every tweet is not feasible. Hence, summarization of microblogs posted during emergency events has become an important problem in recent years. Several summarization algorithms have been proposed in the literature, both for general document summarization, as well as specifically for summarization of microblogs. However, to our knowledge, there has not been any systematic analysis on which algorithms are more suitable for summarization of microblogs posted during disasters. In this work, we evaluate and compare the performance of 8 extractive summarization algorithms in the application of summarizing microblogs posted during emergency events. Apart from comparing the performances of the algorithms, we also find significant differences among the summaries produced by different algorithms over the same input data.