Microblogging Site Research Articles

An Integrated Approach for Identifying Topical Experts in Microblogs

In the recent times, usage of Social Networking and Micro-blogging sites by the people has increased exponentially. Identifying the right topical experts in a particular domain is a tedious task as huge amount of data being processed. The existing approaches limited in accuracy, which leads to providing irrelevant results. Furthermore, few deal with syntactic similarity and not the semantic analysis. In this paper, we introduce an integrated approach which combines focus crawling, semantic analysis and personalized page ranking algorithm to provide top-k ranked results for a topic. Twitter data is taken as input and the twitter lists are extracted to construct the endorsement graph. Links in the graph are weighted using personalized PageRank. Since social network itself has a huge chunk of data, it is obvious for the endorsement graph to have quite large number of nodes. To increase the scalability and performance, focused crawling is applied before the PageRank calculation. The semantic similarity is calculated between the given input query and the Twitter list name/description to yield better results. We compare the results with Twitter’s WTF (Who To Follow) algorithm to showcase that the proposed system provides more obvious and serendipitous topical experts.

Sentiment Analysis using SVM: A Systematic Literature Review

The world has revolutionized and phased into a new era, an era which upholds the true essence of technology and digitalization. As the market has evolved at a staggering scale, it is must to exploit and inherit the advantages and opportunities, it provides. With the advent of web 2.0, considering the scalability and unbounded reach that it provides, it is detrimental for an organization to not to adopt the new techniques in the competitive stakes that this emerging virtual world has set along with its advantages. The transformed and highly intelligent data mining approaches now allow organizations to collect, categorize, and analyze users’ reviews and comments from micro-blogging sites regarding their services and products. This type of analysis makes those organizations capable to assess, what the consumers want, what they disapprove of, and what measures can be taken to sustain and improve the performance of products and services. This study focuses on critical analysis of the literature from year 2012 to 2017 on sentiment analysis by using SVM (support vector machine). SVM is one of the widely used supervised machine learning techniques for text classification. This systematic review will serve the scholars and researchers to analyze the latest work of sentiment analysis with SVM as well as provide them a baseline for future trends and comparisons.

Emotion-based topic impact on social media

The increasing use of micro-blogging sites have made them very rich data repositories. Information generated is dynamic by nature, tied to temporal conditions and the subjectivity of its users. Everyday life experiences, discussions or events have a direct impact on the behaviours reflected in social networks. It has become important to asses to which degree are these interactions affecting a social group. A possibility is to analyse how impactful a topic is according to the behaviour presented on a social network over time. It is then necessary to develop methods that can contribute towards this task. Having identified a topic in social media, we can obtain a general summary of the emotions it is generating over a social group. We then propose a topic impact score which will be given to each topic based on how this emotions transition, for how many time they span and how many users they reach. This lays ground to quantify how impactful a topic is over a social group, specifically regarding events detected on twitter.

SoftMax based User Attitude Detection Algorithm for Sentimental Analysis

The social microblogging sites empower users to more boldly express their views and reviews on various topics. This has led to the development of a new subdomain in the field of sentiment analysis which can be used for the benefit of various business. The business values associated with mining the user sentiments havefueled a profusion of researchers, companies etc. to get involved in the process to build, update and even promote their products. A majority of the existing methods utilize only the semantics in the user comments to mine the sentiments. The reliability of the comment is not taken into account in the previously existing methods which can severely undermine the sole purpose of sentiment analysis. In this work, SoftMax based attitude detection algorithm is proposed to identify the user nature efficiently. The reliability and the accuracy of the sentiment prediction can be substantially increased with taking user attitude into account. The proposed algorithm is evaluated on tweets fetched from micro blogging website twitter.

Mining Multilingual and Multiscript Twitter Data: Unleashing the Language and Script Barrier

Micro-blogging sites like Twitter have become an opinion hub where views on diverse topics are expressed. Interpreting, comprehending and analysing this emotion-rich information can unearth many valuable insights. The job is trivial if the tweets are in English. But lately, increase in native languages for communication has imposed a great challenge in social media mining. Things become more complicated when people use Roman scripts to write non-English languages. India, being a country with a diverse collection of scripts and languages, encounters the problem severely. We have developed a system that automatically identifies and classifies native tweets, irrespective of the script used. Converting all tweets to English, we get rid of the 'script vs language' problem. The new approach we formulated consists of Script Identification, Language analysis, and Clustered mining. Considering English and the top two Indian languages, we found that the proposed framework gives better precision than the prevailing approaches.

Between Knowledge and Power: Political Agenda and Social Engagement of Political Elites through Micro-Blogging (Twitter) in India

In the age of connectivity through the double-edge sword in the context of time biased and space biased media, new social media emerged as the most powerful weapon in the hands of political elites to create a unique platform where they can interplay between the knowledge and power. This paper is an attempt to understand national political agenda and social engagement of ten top and most popular political elites in the micro-blogging site twitter in India. Though there are other popular social media sites, twitter is known as the mouth piece of some most influential political elites across all major political parties and can give a minute to minute update to their followers. As the part of data collection and data analysis, tweets from selected ten twitter accounts were collected over a period of one month, before the declaration of one of the national political party’s Prime Ministerial (PM) candidate for the upcoming national election of India and being analyzed by adopting hermeneutics as the method of data analysis to understand the lifeworld of the political elites while balancing the knowledge and power.

The Research of Micro-Blog User Preferences based on Classification Technology

With the growing popularity of micro-blog, micro-blog website has become the issue of mass information, research on micro-blog needs from a single user analysis of the relationship between the transition to micro-blog users and forwarding the contents of mining, this paper presents a new method for mining micro-blog user interest, will be excavated the user's interest, how to excavate user's interest preference from massive information. For example, how can we speculate that users are interested in electronic products or food is interested? At the same time, it is necessary to recommend information. To identify the user's interest, through the user's micro-blog, study the techniques for mining user based on the classification method. For example you can use the user belongs to the circle of interest of interested people recommend, or according to the characteristics of the circle group analysis of the user's personal interests, the social networking platform, how massive data automatically in mining circle is a very useful basis function.

Managing Disruptive Behavior through Non-Hierarchical Governance

Disruptive behaviors such as flaming and vandalism have been part of the Internet since its beginning. Various models of hierarchical governance have been established and managed in different online venues, with both successes and failures. Recently, a new model of non-hierarchical governance has emerged using crowdsourcing technology to allow an online community to manage itself. How do people view and work with non-hierarchical governance? In this paper, we present an interview study with people from two sites: the video game League of Legends and Weibo, a microblogging site in China. We found that people were passionate about participation in crowdsourcing, but at the same time, struggled with the system, and acted beyond their designated role within the system. We derive implications for designing online non-hierarchical governance from our research.

Using social media for classifying actionable insights in disaster scenario

Micro-blogging sites are important source of real-time situational information during disasters such as earthquakes, hurricanes, wildfires, flood etc. Such disasters cause miseries in the lives of affected people. Timely identification of steps needed to help the affected people in such situations can mitigate those miseries to a large extent. In this paper, we focus on the problem of automated classification of disaster related tweets to a set of predefined categories. Some example categories considered are resource availability, resource requirement, infrastructure damage etc. Proper annotation of the tweets with these class information can help in timely determination of the steps needed to be taken to address the concerns of the people in the affected areas. Depending on the information category, different feature sets might be useful for proper identification of posts belonging to that category. In this work, we define multiple feature sets and use them with various supervised classification algorithms from literature to study the effectiveness of our approach in annotating the tweets with their appropriate information categories.

Are tweets useful in the bug fixing process? An empirical study on Firefox and Chrome

When encountering an issue, technical users (e.g., developers) usually file the issue report to the issue tracking systems. But non-technical end-users are more likely to express their opinions on social network platforms, such as Twitter. For software systems (e.g., Firefox and Chrome) that have a high exposure to millions of non-technical end-users, it is important to monitor and solve issues observed by a large user base. The widely used micro-blogging site (i.e., Twitter) has millions of active users. Therefore, it can provide instant feedback on products to the developers. In this paper, we investigate whether social networks (i.e., Twitter) can improve the bug fixing process by analyzing the short messages posted by end-users on Twitter (i.e., tweets). We propose an approach to remove noisy tweets, and map the remaining tweets to bug reports. We conduct an empirical study to investigate the usefulness of Twitter in the bug fixing process. We choose two widely adopted browsers (i.e., Firefox and Chrome) that are also large and rapidly released software systems. We find that issue reports are not treated differently regardless whether users tweet about the issue or not, except that Firefox developers tend to label an issue as more severe if users tweet about it. The feedback from Firefox contributors confirms that the tweets are not currently leveraged in the bug fixing process, due to the challenges associated to discovering bugs through Twitter. Moreover, we observe that many issues are posted on Twitter earlier than on issue tracking systems. More specifically, at least one third of issues could have been reported to developers 8.2 days and 7.6 days earlier in Firefox and Chrome, respectively. In conclusion, tweets are useful in providing earlier acknowledgment of issues, which developers can potentially use to focus their efforts on the issues impacting a large user-base.

CHARACTERIZATION OF VALUABLE INFORMATION FROM SOCIAL MEDIA NETWORKS DURING NATURAL DISASTERS

Twitter, a microblog site, have emerged as a new source for detecting and monitoring disaster events specifically earthquakes. The data streamed in Twitter can be used to pull actionable data for emergency response and relief operation. However, no effort has been made to classify data in conversational and informative form which has been used as a common reference for the decision-makers to streamline priorities and activities during a disaster. This paper makes an initial effort in classifying tweets by examining more than 10,000 tweets generated using the hashtags, #Lindol, #EarthquakePH, #Mindanao and the word ‘Lindol’ as a mention. The results are generated using Rapidminer software and deemed as necessary and useful for the disaster management unit. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery of disaster. Article DOI: https://dx.doi.org/10.20319/mijst.2017.32.493505 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Influential user weighted sentiment analysis on topic based microblogging community

Nowadays, social microblogging services have become a popular expression platform of what people think. People use these platforms to produce content on different topics from finance, politics and sports to sociological fields in real-time. With the proliferation of social microblogging sites, the massive amount of opinion texts have become available in digital forms, thus enabling research on sentiment analysis to both deepen and broaden in different sociological fields. Previous sentiment analysis research on microblogging services generally focused on text as the unique source of information, and did not consider the social microblogging service network information. Inspired by the social network analysis research and sentiment analysis studies, we find that people’s trust in a community have an important place in determining the community’s sentiment polarity about a topic. When studies in the literature are examined, it is seen that trusted users in a community are actually influential users. Hence, we propose a novel sentiment analysis approach that takes into account the social network information as well. We concentrate on the effect of influential users on the sentiment polarity of a topic based microblogging community. Our approach extends the classical sentiment analysis methods, which only consider text content, by adding a novel PageRank-based influential user finding algorithm. We have carried out a comprehensive empirical study of two real-world Twitter datasets to analyze the correlation between the mood of the financial social community and the behavior of the stock exchange of Turkey, namely BIST100, using Pearson correlation coefficient method. Experimental results validate our assumptions and show that the proposed sentiment analysis method is more effective in finding topic based microblogging community’s sentiment polarity.

A Distributed Multi-facet Search Engine of Microblogs Based on SolrCloud

Microblog services, such as Twitter and Weibo in China, has become a new yet powerful information dissemination channel. More than 500 million tweets are sent every day. The extraordinary large number of messages brings new challenges to conventional search paradigms: a message might be relevant to the query in many aspects, for example the content, time and location of a message. Furthermore, there might be a large number of such relevant messages. In order to address these challenges, we designed a multi-facet distributed microblog search system using off-the-shelf open source frameworks including SolrCloud, Hadoop and Zookeeper. The system was then populated with real world messages collected from the most popular microblog website in China: Sina Weibo. We compared the performances of the standalone and the distributed version of the system. Empirical experimental results showed both effectiveness and efficiency of the proposed system in retrieving large scale microblog messages.

A "Glee"-ful collaboration: Academic networking in the Tumblr world

Four authors representing disparate disciplines recount their experiences meeting each other through Glee (2009–15) fandom via Tumblr, with an exploration of the popular microblogging site's utility as a platform for fan scholarship. This aspect of the site has been understudied; we thus explore the ways that Tumblr's particular culture and features support professional networking, facilitate the researching of fandom practices, and provide support mechanisms for scholars. In discussing the pleasures and challenges that Tumblr provides the acafan researcher, we weigh whether academia needs to rethink its own paradigms for audience engagement and professional development.

Robust Spammer Detection in Microblogs

Microblogging Web sites, such as Twitter and Sina Weibo, have become popular platforms for socializing and sharing information in recent years. Spammers have also discovered this new opportunity to unfairly overpower normal users with unsolicited content, namely social spams. Although it is intuitive for everyone to follow legitimate users, recent studies show that both legitimate users and spammers follow spammers for different reasons. Evidence of users seeking spammers on purpose is also observed. We regard this behavior as useful information for spammer detection. In this article, we approach the problem of spammer detection by leveraging the “carefulness” of users, which indicates how careful a user is when she is about to follow a potential spammer. We propose a framework to measure the carefulness and develop a supervised learning algorithm to estimate it based on known spammers and legitimate users. We illustrate how the robustness of the detection algorithms can be improved with aid of the proposed measure. Evaluation on two real datasets from Sina Weibo and Twitter with millions of users are performed, as well as an online test on Sina Weibo. The results show that our approach indeed captures the carefulness, and it is effective for detecting spammers. In addition, we find that our measure is also beneficial for other applications, such as link prediction.

Consumer insight mining: Aspect based Twitter opinion mining of mobile phone reviews

Micro-blogging sites such as Twitter are often considered as rich source of opinions of the masses towards products. The character length limit in tweets encourages people to use emojis, emoticons and out of vocabulary words. Due to the huge volume of tweets being generated, it is difficult to manually label tweets and create a supervised learning model for sentiment analysis. Looking into these challenges, the research paper aims to create a feature level sentiment analysis model for Twitter data mining including features such as emoji detection, spelling correction and emoticon detection. The proposed model consists of automated training data labeling by using lexicon based approach. It is an ontology based system with the domain of “Smartphone”. In addition to the general lexicon used, a set of lexicons specific for each attribute of the domain “Smartphone” are used to improve classification accuracy for training data generation. This is used to classify tweets obtained about a particular mobile phone using SVM classifier. Experimental results show that the classifier based on automated training data provides good accuracy. It also demonstrates the importance of emoji detection and the attribute specific lexicons which help improve the classification accuracy.

Open source social media intelligence for enabling government applications

Open-source social media intelligence (OSSMInt) is a field comprising of techniques and applications to analyze and mine open-source social media data for extracting actionable information and useful insights. The focus of the work presented in this paper is on novel applications and techniques of OSSMInt in the government sector. I propose and develop several novel use-cases and applications around OSSMInt for government and broadly divide them into three categories: identification, prediction, and response applications. In particular, I present solutions, tools and techniques for analyzing data from micro-blogging website to analyze citizen complaints and grievances in the public sector [response]. The research presented in this paper also describes my work on analyzing data from Twitter micro-blogging website to early forecast a civil unrest and protest [prediction]. Furthermore, I build various applications around identification and detection that are useful for the government and security analysts. I demonstrate the application of OSSMInt for identifying religious conflicts within society by mining public opinions on Tumblr website and fill the gaps of offline surveys. The study presented in this paper propose solutions for enabling law enforcement agencies to detect, prevent and combat online radicalization and extremism (content, users, and communities) by mining data from Tumblr, Twitter and YouTube websites [identification]. Furthermore, I also propose and build an application for detecting secret message exchanged in an adversarial communication and capture the obfuscated terms in messages. The central component of my proposed solution approach is the application of information retrieval and machine learning based techniques and algorithms. The study consists of experimenting with a diverse range of machine learning algorithms such as unsupervised, semi-supervised and supervised learning (k-NN, SVM, naive Bayes, random forest and decision tree) based algorithms. Based on the experimental results, I observe that there are several untapped opportunities as well as technical challenges in exploiting open-source social media data by the government for intelligence gathering. Furthermore, social media is a rich source of information for building security informatics based applications by mining the content, users, links and relationships on social media platforms.

Identifying High Quality Document–Summary Pairs through Text Matching

Text summarization namely, automatically generating a short summary of a given document, is a difficult task in natural language processing. Nowadays, deep learning as a new technique has gradually been deployed for text summarization, but there is still a lack of large-scale high quality datasets for this technique. In this paper, we proposed a novel deep learning method to identify high quality document–summary pairs for building a large-scale pairs dataset. Concretely, a long short-term memory (LSTM)-based model was designed to measure the quality of document–summary pairs. In order to leverage information across all parts of each document, we further proposed an improved LSTM-based model by removing the forget gate in the LSTM unit. Experiments conducted on the training set and the test set built upon Sina Weibo (a Chinese microblog website similar to Twitter) showed that the LSTM-based models significantly outperformed baseline models with regard to the area under receiver operating characteristic curve (AUC) value.

An Evaluation Method for Content Analysis Based on Twitter Content Influence

Twitter is a microblogging website, which has different characteristics from any other social networking service (SNS) in that it has one-directional relationships between users with short posts of less than 140 characters. These characteristics make Twitter not only a social network but also a news media. In addition, Twitter posts have been used and analyzed in various fields such as marketing, prediction of presidential elections, and requirement analysis. With an increase in Twitter usage, we need a more effective method to analyze Twitter content. In this paper, we propose a method for content analysis based on the influence of Twitter content. For measuring Twitter influence, we use the number of followers of the content author, retweet count, and currency of time. We perform experiments to compare the proposed method, frequency, numerical statistics, user influence, and sentiment score. The results show that the proposed method is slightly better than the other methods. In addition, we discuss Twitter characteristics and a method for an effective analysis of Twitter content.

User Vitality Ranking and Prediction in Social Networking Services: A Dynamic Network Perspective

Social networking services have been prevalent at many online communities such as Twitter.com and Weibo.com, where millions of users keep interacting with each other every day. One interesting and important problem in the social networking services is to rank users based on their vitality in a timely fashion. An accurate ranking list of user vitality could benefit many parties in social network services such as the ads providers and site operators. Although it is very promising to obtain a vitality-based ranking list of users, there are many technical challenges due to the large scale and dynamics of social networking data. In this paper, we propose a unique perspective to achieve this goal, which is quantifying user vitality by analyzing the dynamic interactions among users on social networks. Examples of social network include but are not limited to social networks in microblog sites and academical collaboration networks. Intuitively, if a user has many interactions with his friends within a time period and most of his friends do not have many interactions with their friends simultaneously, it is very likely that this user has high vitality. Based on this idea, we develop quantitative measurements for user vitality and propose our first algorithm for ranking users based vitality. Also, we further consider the mutual influence between users while computing the vitality measurements and propose the second ranking algorithm, which computes user vitality in an iterative way. Other than user vitality ranking, we also introduce a vitality prediction problem, which is also of great importance for many applications in social networking services. Along this line, we develop a customized prediction model to solve the vitality prediction problem. To evaluate the performance of our algorithms, we collect two dynamic social network data sets. The experimental results with both data sets clearly demonstrate the advantage of our ranking and prediction methods.