Microblogging Service Research Articles

Social media based event summarization by user–text–image co-clustering

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.

A Hybrid Approach for Detecting Automated Spammers in Twitter

Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and large user base are frequently exploited by automated spammers, content polluters, and other ill-intended users to commit various cybercrimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to address these problems. However, most of these approaches are based on user characterization and completely disregarding mutual interactions. In this paper, we present a hybrid approach for detecting automated spammers by amalgamating community-based features with other feature categories, namely metadata- , content- , and interaction-based features. The novelty of the proposed approach lies in the characterization of users based on their interactions with their followers given that a user can evade features that are related to his/her own activities, but evading those based on the followers is difficult. Nineteen different features, including six newly defined features and two redefined features, are identified for learning three classifiers, namely, random forest , decision tree , and Bayesian network , on a real dataset that comprises benign users and spammers. The discrimination power of different feature categories is also analyzed, and interaction- and community-based features are determined to be the most effective for spam detection, whereas metadata-based features are proven to be the least effective.

A diary study of information encountering triggered by visual stimuli on micro-blogging services

Despite various models established to present the process of information encountering (IE), little research has been done on the stimulus that plays an essential role in attracting users’ attention and eliciting the subsequent behavioral responses during an IE process. This study was particularly interested in visual stimuli which are superior to textual ones in enhancing information processing and sensory experience. A diary study of IE was conducted in the context of micro-blogging services. They demonstrate the environmental characteristics conducive to IE and are especially abundant in visual stimuli. A total of 189 valid IE incidents triggered by visual stimuli on a representative micro-blogging service were collected with an online questionnaire created based on the critical incident technique (CIT) and analyzed both quantitatively and qualitatively. As found in this study, most of the visual stimuli triggering IE excluded text or motion, and their comprehensibility and novelty were both perceived to be high while humorousness much lower. The encountered micro-posts covered a wide range of topics and were published by different types of micro-bloggers. When interacting with the posts, the participants sometimes just examined their visual or textual content, but sometimes further captured the posts by liking, reposting, commenting, and following, etc. The significant results indicate that the visual stimuli excluding text and those with higher comprehensibility or humorousness were more likely to induce intense approach to the micro-posts. These findings inform micro-bloggers of the means of engaging the audience in intense interactions with their posts to gain persistent profit or reputation. The combination of diaries and the CIT is effective for data collection in IE research.

Can microblogging information disclosure reduce stock price synchronicity? Evidence from China

We study the relationship between stock price synchronicity and information disclosure of firms listed in the Chinese stock market, using hand-collected data on firms’ official microblogging content in Sina Weibo, a popular microblogging service in China. We find that after controlling for the impact of traditional media, the number of Weibo tweets is related negatively to stock price synchronicity, indicating that stock prices incorporate firm-specific information disclosed in the firm’s official Weibo. Number of microblogging fans can strengthen this negative relationship. Our result is robust to alternative measures of stock price synchronicity, microblogging information disclosure, and to endogeneity issues. JEL Classification: G14, G15

Data Analytics Applications for Streaming Data From Social Media: What to Predict?

Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data? In this review, we are focusing on microblogging services and discuss applications of streaming data from the scientific literature. We will focus on text-based approaches because they represent by far the largest cohort of studies and we present a taxonomy of studied problems.

Effects of perceived online–offline integration and internet censorship on mobile government microblogging service continuance: A gratification perspective

The success of government social media relies on citizens' continual usage. Through the theoretical lens of the Uses & Gratifications theory, we develop a research model for mobile government microblogging services (GMS) continuance intention, in which citizens' perceived integration of online and offline social lives and internet censorship are incorporated. Based on data from 629 mobile GMS users in China, the results of the structural equation modeling analysis show that perceived online–offline integration has a significant and positive effect on the three dimensions of gratification: content gratification, social gratification, and process gratification, which jointly influence mobile GMS continuance. Citizens' perceived internet censorship negatively affects their mobile GMS continuance. These results might provide understanding about the unique aspects of government social media, especially in the context of internet censorship.

Inferring user interests in microblogging social networks: a survey

With the growing popularity of microblogging services such as Twitter in recent years, an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications and areas. Inferring user interests plays a significant role in providing personalized recommendations on microblogging services, and also on third-party applications providing social logins via these services, especially in cold-start situations. In this survey, we review user modeling strategies with respect to inferring user interests from previous studies. To this end, we focus on four dimensions of inferring user interest profiles: (1) data collection, (2) representation of user interest profiles, (3) construction and enhancement of user interest profiles, and (4) the evaluation of the constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging social networks with respect to the four dimensions. For each dimension, we review and summarize previous studies based on specified criteria. Finally, we discuss some challenges and opportunities for future work in this research domain.

SGSG: Semantic graph-based storyline generation in Twitter

Twitter is a popular microblogging service that has become a great medium for exploring emerging events and breaking news. Unfortunately, the explosive rate of information entering Twitter makes the users experience information overload. Since a great deal of tweets revolve around news events, summarising the storyline of these events can be advantageous to users, allowing them to conveniently access relevant and key information scattered over numerous tweets and, consequently, draw concise conclusions. A storyline shows the evolution of a story through time and sketches the correlations among its significant events. In this article, we propose a novel framework for generating a storyline of news events from a social point of view. Utilising powerful concepts from graph theory, we identify the significant events, summarise them and generate a coherent storyline of their evolution with reasonable computational cost for large datasets. Our approach models a storyline as a directed tree of socially salient events evolving over time in which nodes represent main events and edges capture the semantic relations between related events. We evaluate our proposed method against human-generated storylines, as well as the previous state-of-the-art storyline generation algorithm, on two large-scale datasets, one consisting of English tweets and the other one consisting of Persian tweets. We find that the results of our method are superior to the previous best algorithm and can be comparable with human-generated storylines.

Why we follow: Examining motivational differences in following sport organizations on Twitter and Weibo

Social media platforms provide a space where sport fans can interact directly with sport organizations; however, researchers have not empirically examined users’ motivation and usage. Guided by uses and gratifications theory, the authors explored whether social media users’ motivations differed when following a sport organization on two similar microblogging services: Twitter and Weibo. Data gathered from an online survey of users who followed the Los Angeles Lakers on both Twitter (n = 299) and Weibo (n = 796) were analyzed using the Multiple Indicators Multiple Causes model. Findings showed that Weibo users had higher motives for obtaining information, entertainment, technical knowledge, passing time, and escaping from their life than Twitter users, while Twitter followers had higher motivations to express team support than their Weibo counterparts. Results of hierarchical multiple regression analysis suggested that Weibo users had high motives in information gathering and expressing team support, and they also tended to be more interactive. Twitter users were more interested in interacting with a sport organization if their motives for obtaining entertainment information and technical information were met. Results of this study will assist sport organizations to learn to understand users’ motives for following a sports team in both China and America and then develop more effective strategies to engage these users in these two main markets.

Challenging prevailing narratives with Twitter: An #AustraliaDay case study of participation, representation and elimination of voice in an archive

Social media, specifically a microblogging service such as Twitter, constitute a public space that has changed how we interact with, exchange, and respond to information in a civil society. They also have the potential to give public voice to minority narratives that are under-represented in the mainstream media, just as grassroots graffiti in public spaces have done over human history. Using a specific case study around an issue in the Australian national discourse around Australia Day, this study contributes new insights towards an understanding of how alternate narratives are expressed and erased in cyberspace, just as graffiti are erased from public view by the authorities. Widely promoted by official sources as a day of festivity and celebration, the Australia Day Your Way initiative actively promotes the use of the hashtag #AustraliaDay to metatag tweets for capture to an annual time capsule stored by the National Museum of Australia. For Australia’s indigenous minority though, Australia Day is symbolic of an entirely different narrative, expressed online through the hashtags #InvasionDay and #SurvivalDay. We studied all three hashtags and their intersections on Twitter and also compared this data to what was showcased in the official time capsule. We found that although the alternative voices existed on Twitter, they were excluded from the official time capsule. This has implications both for archives and for future historians studying contemporary events.

Sentiment Analysis of Health Care Tweets: Review of the Methods Used.

BackgroundTwitter is a microblogging service where users can send and read short 140-character messages called “tweets.” There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field.ObjectiveThe first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed.MethodsA review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy.ResultsA total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study’s final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used.ConclusionsMultiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting–specific corpus of manually annotated tweets first.

Identifying network structure, influencers and social mood in digital spheres: A sentiment and content analysis of down syndrome awareness

Down syndrome is a sensitive subject and one that requires efforts being made to improve conditions for individuals with Down syndrome across multiple dimensions. Social awareness is one of the important dimensions for the inclusion of individuals with Down syndrome. Online spaces, as well as offline spaces, are an important part of our daily lives and these spaces provide numerous opportunities to create, increase and sustain awareness of Down syndrome. From this perspective, this study examines the awareness of Down syndrome in online spaces. With this objective in mind, the research examined 4168 conversations from 2430 individuals that were posted in a microblogging service on World Down Syndrome Day. The research findings indicate that the Down syndrome network is distributed in time and space with diverse backgrounds. Key influencers were those who were already associated with Down syndrome. Furthermore, the social mood for Down syndrome was found to be positive.

Social media use in German visceral surgeons: a cross-sectional study of a national cohort.

Engagement in social media is increasing. Medical professionals have been adapting LinkedIn, a professional networking site, and Twitter, a microblogging service, for a number of uses. This development has been described for a number of medical specialties, but there remains a paucity of European data. A study was undertaken to measure the engagement and activity of German visceral surgeons on social media platforms. Visceral surgeons were identified from 15 regional Associations of Statutory Health Insurance Physicians (Kassenärztliche Vereinigungen) opt-in registers. A manual search was subsequently performed across key professional social media platforms. The presence of a profile and key markers of use were recorded. In total, 575 visceral surgeons were identified. 523 (93%) were men. 183 (31%) surgeons engaged in professional social media. 22 (3.8%) used Twitter, producing a mean of 16.43 tweets with a mean of 7.57 followers. 137 (24%) surgeons had a profile on LinkedIn with a mean of 46.36 connections. Female surgeons were less connected on LinkedIn (P<0.005). 60 (10%) used Xing, with a mean of 27.95 connections. There were no significant differences in use of social media between surgeons from Eastern and Western Germany (P=0.262) or male and female surgeons (P=0.399). German visceral surgeons are less engaged and less active on social media than previously examined cohorts. Loco-regional, cultural, demographic and regulatory matters may have a significant influence on uptake. If this surgical cohort wishes to have a wider international presence then education on the potential benefits of these tools may be needed.

Social Big-Data-Based Content Dissemination in Internet of Vehicles

By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

Advertising to Early Trend Propagators: Evidence from Twitter

In the digital economy, influencing and controlling the spread of information is a key concern for firms. One way firms try to achieve this is to target firm communications to consumers who embrace and propagate the spread of new information on emerging and “trending” topics on social media. However, little is known about whether early trend propagators are indeed responsive to firm-sponsored messages. To explore whether early propagators of trending topics respond to advertising messages, we use data from two field tests conducted by a charity and an emerging fashion firm on the microblogging service Twitter. On Twitter, “promoted tweets” allow advertisers to target individuals based on the content of their recent postings. Twitter continuously identifies in real time which topics are newly popular among Twitter users. In the field tests, we collaborated with a charity and a fashion firm to target ads at consumers who embraced a Twitter trend early in its life cycle by posting about it, and compared their behavior to that of consumers who posted about the same topic later on. Throughout both field tests, we consistently find that early propagators of trends are less responsive to advertising than consumers who embrace trends later. Data and the online appendix are available at https://doi.org/10.1287/mksc.2017.1062 .

Identifying network structure, influencers and social mood in digital spheres: A sentiment and content analysis of down syndrome awareness

AbstractDown syndrome is a sensitive subject and one that requires efforts being made to improve conditions of individuals with Down syndrome across multiple dimensions. Social awareness is one of the important dimensions for the inclusion of individuals with Down syndrome. Online spaces as well as offline spaces are an important part of our daily lives, and these spaces provide numerous opportunities to create, increase and sustain awareness of Down syndrome. From this perspective, this study examines the awareness of Down syndrome in online spaces. With this objective in mind, the research examined 4,168 conversations from 2,430 individuals that were posted in a microblogging service on WDSD. The research findings indicate that the Down syndrome network is distributed in time and space with diverse backgrounds. Key influencers were those who were already associated with Down syndrome. Furthermore, the social mood for Down syndrome was found to be positive.Keywords: Down syndrome, World Down Syndrome Day, Down syndrome awareness, social network analysis, sentiment analysis.*

Hashtag Recommendation Approach Based on Content and User Characteristics

ABSTRACTTwitter has become a popular microblogging service that allows millions of active users share news, emergent social events, personal opinions, etc. That leads to a large amount of data producing every day and the problem of managing tweets becomes extremely difficult. To categorize the tweets and make easily in searching, the users can use the hashtags embedding in their tweets. However, valid hashtags are not restricted which lead to a very heterogeneous set of hashtags created on Twitter, increasing the difficulty of tweet categorization. In this paper, we propose a hashtag recommendation method based on analyzing the content of tweets, user characteristics, and currently popular hashtags on Twitter. The proposed method uses personal profiles of the users to discover the relevant hashtags. First, a combination of tweet contents and user characteristics is used to find the top-k similar tweets. We exploit the content of historical tweets, used hashtags, and the social interaction to build the user profiles. The user characteristics can help to find the close users and enhance the accuracy of finding the similar tweets to extract the hashtag candidates. Then a set of hashtag candidates is ranked based on their popularity in long and short periods. The experiments on tweet data showed that the proposed method significantly improves the performance of hashtag recommendation systems.

Corpus Based Twitter Sentiment Analysis

Social media is the main advertising and analysis tool in today’s world. Twitter is a microblogging service usually used as an instant communication platform. It contains a rich amount of data in semi-structured format. The capacity to provide information in real time has stimulated many companies to use this service to understand their consumers. In this work, a method had been adopted to fetch the data in real time environment directly from twitter and sentiment analysis was performed on the streamed data. To provide scalability and reduced cost of analytics, the work was implemented using the Apache Open Source Platform, Hadoop. Further, the sentiment analysis was conducted based on the dictionary model.

Timeline Analysis of Twitter User

During the previous decade, the use of social-media has increased considerably. The social media or social networking sites are used to build consensus among people. Twitter is one of the most famous social networking site or micro-blogging service where people post their messages defined as tweets. There are nearly 330 million averaged monthly active users as reported in 2017. The data generated by each user is unique and hence the information is associated with their aspects of life. In this paper, extraction of various hidden features related to tweets generated by twitter user is analyzed. The analysis is done to know the behavior insights of a twitter user.

Sentiment Classification of Tweets with Non-Language Features

In recent years, mining social media sites like Twitter, Facebook have become a hot research focus. Twitter is one of the most fashionable microblogging services that permit users to express their views on e-commerce websites, sports, politics, modern technologies, movies, spirituality and so on. Sentiment can be considered as a natural expression of viewer’s perception. It is extremely difficult to identify the sentiment/opinion about a specific product or event by collecting and compiling microblog tasks manually. It is only fair to develop a system which collects, compile and analyse microblog tasks to arrive on insights that helps to take an action against an event. The system can monitor and evaluate in real time online views, to demonstrate how the whole world is reacting to a concept/ideology/event. Developing such a system which assigns polarity to a tweet is a hard task. In this paper we propose a scoring methodology to find the sentiment polarity of the Twitter messages. Emotions, shortened words and non-language features are integrated to increase the significance of the score computed for assigning the polarity for the tweets. The experimental results show that the proposed method enhances the accuracy of the assignment of polarity to tweets.