Social Microblogging Research Articles

Rumour identification on Twitter as a function of novel textual and language-context features.

Social microblogs are one of the popular platforms for information spreading. However, with several advantages, these platforms are being used for spreading rumours. At present, the majority of existing approaches identify rumours at the topic level instead of at the tweet/post level. Moreover, prior studies used the sentiment and linguistic features for rumours identification without considering discrete positive and negative emotions and effective part-of-speech features in content-based approaches. Similarly, the majority of prior studies used content-based approaches for feature generation, and recent context-based approaches were not explored. To cope with these challenges, a robust framework for rumour detection at the tweet level is designed in this paper. The model used word2vec embeddings and bidirectional encoder representations from transformers method (BERT) from context-based and discrete emotions, linguistic, and metadata characteristics from content-based approaches. According to our knowledge, we are the first ones who used these features for rumour identification at the tweet/post level. The framework is tested on four real-life twitter microblog datasets. The results show that the detection model is capable of detecting 97%, 86%, 85%, and 80% of rumours on four datasets respectively. In addition, the proposed framework outperformed the three latest state-of-the-art baselines. BERT model presented the best performance among context-based approaches, and linguistic features are best performing among content-based approaches as a stand-alone model. Moreover, the utilization of two-step feature selection further improves the detection model performance.

Chinese Social Media Platforms and Microblogging in the Context of Public Diplomacy

The term “digital public diplomacy” that appeared in the 21st century owes much to the emergence and development of the concept of Web 2.0 (interactive communication on the Internet). The principle of network interaction, in which the system becomes better with an increase in the number of users and the creation of user-generated content, made it possible to create social media platforms where news and entertainment content is created and moderated by the user. Such platforms have become an expression of the opinions of various groups of people in many countries of the world, including China. The Chinese segment of the Internet is “closed”, and many popular Western services are blocked in it. Studying the structure of Chinese social media platforms and microblogging, as well as analyzing targeted content is necessary to understand China’s public opinion, choose the right message channels and receive feedback for promoting the country’s public diplomacy. This paper reveals the main Chinese social media platforms and microblogging and provides the assessment of their popularity, as well as possibility of analyzing China’s public opinion based on “listening” to social media platforms and microblogging.

Mining Hot-Personae Approach Based on Local Social Microblog Graph

With the increasing popularity of online social media platforms, netizens always chat with their friends and share information, such as what they like in their daily lives, on these platforms. Netizens publish tons of information on social platforms every day. These platforms converge many people and information. The processes by which the publishers find the sharers who are interested in their publications and the sharers find some interesting things and information in what the publishers published have resulted in the challenge of retrieving information from social network fields. To address these issues, we propose a novel algorithm, named Hot Persona Mining, to analyze the users' focus personae from microblog posts in the online social networks. During mining, we first utilize local-based graph clustering to establish the nearest neighbor nodes of target users. Then, we mine users' focused personae entities from their neighbors' published microblog posts in different periods. Then, we construct the users' active score vector and their interest matrix to mine the hot personae in every local social graph. The experimental results show that our algorithm effectively mines current focus of the target user, and exhibits good performance as shown by its precision, recall and F-measures.

Using implicitly and explicitly rated online customer reviews to build opinionated Arabic lexicons

Creating an opinionated lexicon is an important step towards a reliable social media analysis system. In this article we are proposing an approach and describing an experiment to build an Arabic polarised lexical database from analysing online implicitly and explicitly rated customer reviews. These reviews are written in modern standard Arabic and Palestinian/Jordanian dialect. Therefore, the produced lexicon contains casual slangs and dialectic entries used by the online community, which is useful for sentiment analysis of informal social media micro-blogs. We have extracted 28,000 entries from processing 15,100 reviews and by expanding the initial lexicon through Google translate. We calculated an implicit rating for every review driven by its text to address the problem of ambiguous opinions of certain online posts, where the text of the review does not match the given rating (the explicit rating). Each entry was given a polarity tag and a confidence score. High confidence scores have increased the precision of the polarisation process. Explicit rating has increased the coverage and confidence of polarity.

Application of Sentiment Analysis in Understanding Human Emotions and Behaviour

INTRODUCTION: Presently, naturalistic observation is considered as critical in understanding, and predicting the complexities of feelings and sentiments. Emerging trends of social media has completely revolutionized the process of communication. Social media, microblogging, and other means of e-communication can be used for extracting the content to decode the quality, valence, and effectiveness of communication. OBJECTIVES: In this paper, we represent the explanatory urge of mental health assessment during a pandemic situation, especially in a smart city scenario. METHODS: We reviewed the role of sentimental analysis, as an emerging application tool for emotional and behavioural analysis for the population affected by a pandemic. This paper examines the prospect of analysing the sentiments through machine learning tools to understand, describe, and predict human behavior. CONCLUSION: Further analysis of this psychological e-content can be used to understand and predict the patterns of human sentiments.

Twitter rumour detection in the health domain

In the last years social networks have emerged as a critical mean for information spreading bringing along several advantages. At the same time, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. For this reason, although the identification in social microblogs of which topic is a rumour has been studied in several works, there is the need to detect if a post is either a rumor or not. In this paper we cope with this last challenge presenting a novel rumour detection system that leverages on newly designed features, including influence potential and network characteristics measures. We tested our approach on a real dataset composed of health-related posts collected from Twitter microblog. We observe promising results, as the system is able to correctly detect about 90% of rumours, with acceptable levels of precision.

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.

Using Tweets Embeddings For Hashtag Recommendation in Twitter

Social microblogging platforms such as Twitter have become hugely popular forms of this latest sort of blogging. Twitter users make and use hashtags in their tweets to categorize them according to topic or theme, likewise to make them ascertainable to other bloggers through search. However, the liberated hashtag creation policy make a wide hardness for bloggers to find appropriates hashtags for their posts. Indeed, the task of recommending hashtags has many benefits to afford; notably it assists users to choose relevant hashtags for their posts in real time, which will save them from a supplementary stress. Actually, the achieve success of several models of neural networks for calculating word embeddings, has driven approaches for generating syntactic and semantic embeddings for long and noisy text, such as paragraphs, sentences and micro-blogs. On the parallel lines, our aim is to develop a hashtag recommender system to assist users to choose relevant hashtags for their posts in real time, based on using semantic embeddings representation of tweets, which we can subsequently use to capture semantic similarity or relatedness between tweets. In the current paper, we introduce an approach to hashtag recommendation in Twitter that is based on the following proceedings: Using a pre-trained word embeddings on a large corpus such as Google News applying one of the famous embeddings methods, Representing a given tweet by a weighted averaging value of its word embeddings, Then combining these features with the DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm, to divide the heterogeneous collection of tweets into clusters that contain syntactically and semantically similar tweets. Afterwards, Recommending the top-K suitable hashtags to the user after computing the similarity between the entered tweet and the centroids of obtained clusters. Our system achieved promising results which demonstrate the effectiveness of our approach.

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.

Using Location-Based Social Media to Chart the Patterns of People Moving between Cities: The Case of Weibo-Users in the Yangtze River Delta

Urban-geographical research using location-based social media (LBSM) has itself been characterized by uneven geographies in that most studies deal with Europe and North America. This implies a relative dearth of studies focusing on countries such as China, and this in spite of the country having the largest number of Internet users in the world. This paper proposes to address this lacuna by showing the research potential of LBSM services associated with Weibo, by far the most popular online social microblogging and networking service in China. To this end, we map inter-city connections within the Yangtze River Delta based on three million individuals’ space-time footprints derived from Weibo. Empirical results reveal that the inter-city connections derived from Weibo present both common and specific spatial patterns associated with inter-city travel. We find that a small percentage of cities and city-dyads constitute the backbone of this inter-city network. The dominant direction of individual flows tends to be from primary cities to sub-primary cities, and from peripheral cities to primary cities. In addition, city-dyad connectivities do not strictly follow cities’ positions in terms of their centralities in the hierarchical distribution. Furthermore, the effects of administrative boundaries and cities’ administrative level are significant. We benchmark these insights by re-examining our findings against the backdrop of polycentric developments in the Yangtze River Delta, which confirms the potential usefulness of LBSM data for analyzing urban-geographical patterns.

User Disposition and Attitude towards Advertisements Placed in Facebook, LinkedIn, Twitter and YouTube

An analytical understanding of user response to advertisements published in social media websites is very interesting from both academic and marketing perspectives. Present research administers two studies; study 1; explores user perceptions of advertisements in four areas of social media, viz. social networking (Facebook), business networking (LinkedIn), microblogging (Twitter) and video sharing (YouTube) dependent to their demographic and lifestyle characteristics, study 2; empirically identifies is there any significant mean difference with the perceived advertising value and attitude across the four social media websites. The data culled from 710 usable responses were used to empirically examine the objectives of this research. The decision tree technique (classification tree) using CHAID criterion and MANOVA was employed to study 1 and study 2 respectively. The results identified that; the four sites showed differing patterns of user attitude towards advertisements published in them (b) both advertising value and attitude compositely differ across the four social media websites.

Brand Clustering Based on Social Big Data: A Case Study

Since the Internet provides a common way for expressing and sharing people’s ideas or minds, corporate marketers can gather data on the web to acquire measurable and actionable insights. As the smartphones are widely spread as hand-held devices, SNSs(Social Network Services) in the smartphones become common media to record people’s daily activities and thoughts, researchers try to gather and read people’s mind on SNS through opinion mining or sentiment analysis. In this study we suggest a framework for clustering brand names and perform a case study of cosmetic products using social big data gathered on the social media Microblog, Twitter. To cluster the brand names, we calculate the distance of paired brand names based on the total number paired brand names mentioned together. To identify the clusters among these brands names, we projected the brand names onto a 2dimensional and a 3-dimentional space using MDS(Multi-Dimensional Scaling). After projecting the brand names, we found the clusters of the brand names using k-means clustering and identified the characteristics of each cluster.

Structure in the Tweet Haystack: Uncovering the Link between Text-Based Sentiment Signals and Financial Markets

We examine the relationship between signals derived from unstructured social media microblog text data and financial market developments. Employing statistical language modeling techniques we construct directional user sentiment and non-directional topic disagreement metrics and link these to S&P 500 index returns and volatility. Based on an extensive five year sample of Twitter messages our study shows that both unsupervised and supervised statistical learning methods successfully identify subsets of expert users with distinct finance focus. This allows to filter out the substantial noise associated with social media. Accounting for salient properties of the time series in ARMA models we document significant effects of expert disagreement signals on current and future S&P volatility. Moreover, we detect a significant contemporaneous relation between expert sentiment signals and S&P returns.

Social microblog TWITTER use to motivate young people (NEETs) to involve in distance education

Across the world, young people face real and increasing difficulty in finding decent work with each passing day. Youth unemployment is not a new phenomenon; it is part of the global jobs crisis. Two additional emerging factors are worsening the youth employment crisis even further and causing challenges in the transition to decent work:  Increase in “discouraged youth”, in other words young people “neither in education nor in employment or training” (NEET).  Rising unemployment among university graduates, and more generally graduates of tertiary education institutions. What can be done to solve the dropout crisis with microblogging possibilities? Bloggers are driven to document their lives, provide commentary and opinions, express deeply felt emotions,articulate ideas through writing, and form andmaintain community forums. Using Twitter is becoming more relevant than ever, but we need to be aware of how any given audience engages with Twitter. Many educators have found that Twitter is a great tool to help increase participation and continue to engage students once they’ve left the classroom.The use of e-learning in itself does not constitute an enhancement of the quality of teaching and learning, but it is a potential enabler for such enhancement. Examples of actual good practice have been provided to illustrate how certain initiatives have addressed challenges and sought to engage learners in an effective manner.

When Treatments are Tweets: A Network Mobilization Experiment over Twitter

This study rigorously compares the effectiveness of online mobilization appeals via two randomized field experiments conducted over the social microblogging service Twitter. In the process, we demonstrate a methodological innovation designed to capture social effects by exogenously inducing network behavior. In both experiments, we find that direct, private messages to followers of a nonprofit advocacy organization’s Twitter account are highly effective at increasing support for an online petition. Surprisingly, public tweets have no effect at all. We additionally randomize the private messages to prime subjects with either a “follower” or an “organizer” identity but find no evidence that this affects the likelihood of signing the petition. Finally, in the second experiment, followers of subjects induced to tweet a link to the petition are more likely to sign it—evidence of a campaign gone “viral.” In presenting these results, we contribute to a nascent body of experimental literature exploring political behavior in online social media.

Information fusion methods for the automatic creation of Twitter lists

Many microblogging services provide tools that allow users to organise the people they follow into groups for easier information access and filtering. However, the uptake of these tools is low with a likely reason being that curating groups of followees is a time consuming task. This paper proposes methods for automatically clustering followees into groups so that users can use these groups as their user lists. As social microblogging services contain both textual content posted by users and directed followee relationships between users, members in the same list usually share common interests and/or have dense followee relationships. Under this assumption, this paper first applies separate content- and graph-based methods to cluster users. Next, we propose several novel information fusion configurations that combine textual and network features. We evaluate these approaches using both an offline evaluation and a user evaluation on datasets crawled using the Twitter API.

Tweeting to Learn: Understanding Twitter Through the Lens of Connectivism

Twitter, primarily a social media outlet, has recently started foraying and gaining a foothold in higher education. Written from a student perspective, this paper attempts to explain and critically discuss the usage and popularity of Twitter as a tool of active learning in higher education. The author writes about her student experience of using Twitter in her Public Relations Communications graduate class. The paper explores and scrutinizes the social media channel within the theoretical framework of connectivism. Twitter appeared on the contemporary education scene in late October 2006. Originally developed as a social messaging microblogging tool, Twitter found its way into higher education institutions (Grosseck & Holotescu, 2008). At present, a growing number of higher educational institutions all over the world are teaching courses through this microblogging social media application. Twitter is a microblogging tool which allows users to send brief but informative messages to each other in fewer than 140 characters (Grosseck & Holotescu, 2008). The messages are called tweets, the people who tweet are called twitterers, and the people who read these tweets and respond to them are called users. It allows for real-time communication, diverse interaction, discussion with collaboration, and sharing of experiences and opinions (Skiba, 2008). Twitter has become a popular teaching tool that provides for active learning, learner attention, learner discovery, learner participation, and learner knowledge schema development. Written from a graduate student’s viewpoint, this paper examines and discusses the usage of the educational technological tool of Twitter in the classroom. The author writes in depth about her experience and observation of using Twitter in her graduate Public Relations class at an international university in south-east USA. She explains the role and usage of Twitter using the contemporary online learning theory of connectivism. The paper is divided into three sections. Section One provides a brief explanation of connectivism theory, followed by the author’s story in Section Two. The third and final section of the paper embodies the discussion where the connectivism theory is revisited in greater depth. Through her paper, the author hopes to generate relevant and meaningful debate about using Twitter as a method of student engagement in graduate classrooms.

Nutzergenerierte Reisewarnungen auf Twitter: Eine Explorative Analyse / User-Generated Travel Warnings on Twitter: An Explorative Analysis

ZusammenfassungViele internationale auswärtige Ämter nutzen die Online-Plattform Twitter mittlerweile als Kanal für Reisewarnungen. Dieser neue Informationskanal erlaubt den Betrieb von Anwendungen, die einem breiten Publikum aktuelle, hochqualitative Reiserisikoinformationen zugänglich machen. Unsere explorative Forschung hat das Ziel, die Rolle der Informationsquelle und Zielgruppe einer solchen Anwendung zu untersuchen. Die Resultate unserer Analyse zeigen keine substantiellen Hinweise darauf, dass nutzergenerierte Inhalte per se ungeeignet als Informationsquelle sind. Zusätzlich zeigt unsere Analyse, dass Vielreisende eine besonders interessante Zielgruppe sind.

SNS use by the Korean government: a case of Me2Day

This study examines the debates and discussions surrounding the Korean government's Me2Day, the largest social networking and microblogging site in the country used by 26 government bureaus, including the Blue House. Me2Day users post messages composed of up to 150 characters and establish links with other users through multimedia-based information and knowledge obtained from their browsers, desktop computers, e-mail messages, and mobile phones. This study provides a better understanding of the characteristics and structures of cyberspace communication between the Korean government and citizens. The results indicate that although each bureau had different goals in terms of using Me2Day, users were more likely to use the service if their bureau actively posted messages or responded to user comments. Users tended to have critical attitudes toward the Korean government when they were allowed to freely post messages and share information.

Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.