Detecting Streaming of Twitter Spam Using Hybrid Method

Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or websites and promote them to several users. To overcome the problem many researchers proposed some ideas using some machine learning algorithms to detect the spammers. In this research work, a new hybrid approach is proposed to detect the streaming of Twitter spam in a real-time using the combination of a Decision tree, Particle Swarm Optimization and Genetic algorithm. Twitter has given access to the researchers to get tweets from its Twitter-API for real-time streaming of tweet data which they can get direct access to public tweets. Here 600 million tweets are created by using URL based security tool and further some features are extracted for representation of tweets in real-time detection of spam. In addition, our research results are compared with other hybrid algorithms which a better detection rate is given by our proposed work.     

Uncovering sentiment and retweet patterns of disaster-related tweets from a spatiotemporal perspective – A case study of Hurricane Harvey

Social media has been widely used for emergency communication both in disaster-affected areas and unaffected areas. Comparing emotional reaction and information propagation between on-site users and off-site users from a spatiotemporal perspective can help better comprehend collective human behavior during natural disasters. In this study, we investigate sentiment and retweet patterns of disaster-affected areas and disaster-unaffected areas at different stages of Hurricane Harvey. The results show that off-site tweets were more negative than on-site tweets, especially during the disaster. As for retweet patterns, indifferent-neutral and positive tweets spread broader than mixed-neutral and negative tweets. However, negative tweets spread faster than positive tweets, which reveals that social media users were more sensitive to negative information in disaster situations. With the development of the disaster, social media users were more sensitive to on-site positive messages than off-site negative posts. This data-driven study reveals the significant effect of sentiment expression on the publication and re-distribution of disaster-related messages. It generates implications for emergency communication and disaster management.     

基于转发传播过程的微博转发量预测

微博已经成为日常生活中最流行的信息分享工具。转发是微博中信息传播的核心方法,所以转发量预测不仅是一个有趣的研究问题,也有较大的实际意义。然而,当前大部分研究只是把问题视为分类或回归问题,没有考虑转发的传播过程。本文中,我们提出一个符合转发传播过程的转发量预测模型。本文认为转发信息来自两方面：直接粉丝和间接粉丝,而粉丝带来的转发量由转发意愿和影响力决定。我们用历史行为和内容相关性来估算一名直接粉丝的转发意愿,并用他／她的影响力来估算通过他／她的间接粉丝的转发量。新浪微博上的实验表明我们的模型比其他已有的方法效果好。     

Impact of Tweets on Box Office Revenue: Focusing on When Tweets are Written

This study investigates the impact of tweets on box office revenue. Specifically, the study focuses on the times when tweets were written by examining the impact of pre‐ and post‐consumption tweets on box office revenue; an examination that is based on Expectation Confirmation Theory. The study also investigates the impact of intention tweets versus subjective tweets and the impact of negative tweets on box office revenue. Targeting 120 movies released in the US between February and August 2012, this study collected tweet information on a daily basis from two weeks before the opening until the closing and box office revenue information. The results indicate that the disconfirmation that occurs in relation to the total number of pre‐consumption tweets for a movie has a negative impact on box office revenue. This premise suggests that the formation of higher expectations of a movie does not always result in positive results in situations where tweets on perceived movie quality after watching spread rapidly. This study also reveals that intention tweets have stronger effects on box office revenue than subjective tweets.     

一种融合用户关系的自适应微博话题跟踪方法

针对微博口语化、文本短小等特点以及现有研究的不足,本文提出了一种融合用户关系的自适应微博话题跟踪方法.首先,在当前跟踪的时间窗内,推文被映射到特征空间,并作为候选推文集合.然后,针对推文的分布特点以及话题跟踪的目的,变换推文特征空间.在此基础上,利用改进的K-means聚类算法对候选推文集合进行二元聚类,从而划分出相关推文集合,即当前话题目标模型.本文通过Twitter平台获取数据进行实验,实验结果表明,该方法能够实时地跟踪话题热度的变化以及焦点的演变,并提高了微博中话题跟踪的稳定性.该方法为用户推荐、舆情分析等领域提供了有效的支撑.     

Do We Tweet Differently From Our Mobile Devices? A Study of Language Differences on Mobile and Web‐Based Twitter Platforms

Using Twitter as a case study, this article hypothesizes that social media content that is produced on mobile versus web platforms may be qualitatively different. As we increasingly tweet from our smartphones, we may be encouraged to “report” on our immediate thoughts, feelings, physical self, and surroundings. This article seeks to understand whether these presentations of self tend to be more egocentric, negative/positive, gendered, or communal depending on whether they were tweeted from mobile devices or web platforms. Using 6 weeks of Twitter data collected in 2013, we found evidence that users tweet differently from mobile devices and that mobile tweeting is informing new behaviors, attitudes, and linguistic styles online.     

Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis

The use of social media has become an integral part of daily routine in modern society. Social media portals offer powerful public platforms where people can freely share their opinions and feelings about various topics with large crowds. In the current study, we investigated the public opinions and sentiments towards the Syrian refugee crisis, which has affected millions of people and has become a widely discussed, polarizing topic in social media around the world. To analyze public sentiments about the topic on Twitter, we collected a total of 2381,297 relevant tweets in two languages including Turkish and English. Turkish sentiments were considered important as Turkey has welcomed the largest number of Syrian refugees and Turkish tweets carried information to reflect public perception of a refugee hosting country first handedly. We performed a comparative sentiment analysis of retrieved tweets. The results indicated that the sentiments in Turkish tweets were significantly different from the sentiments in English tweets. We found that Turkish tweets carried slightly more positive sentiments towards Syrians and refugees than neutral and negative sentiments, nevertheless the sentiments of tweets were almost evenly distributed among the three major categories. On the other hand, the largest number of English tweets by a significant margin contained neutral sentiments, which was followed by the negative sentiments. In comparison to the ratio of positive sentiments in Turkish tweets, 35% of all Turkish tweets, the proportion of English tweets contained remarkably less positive sentiments towards Syrians and refugees, only 12% of all English tweets.     

ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

Temporal pattern mining from user-generated content

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content; however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available; the link is given in the dataset section.     

基于用户主题模型的微博用户兴趣挖掘(英文)

Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.     

The Twitterization of News Making: Transparency and Journalistic Professionalism

Twitter makes visible some of the most fundamental divides in professional journalism today. It reveals tensions about what constitutes news, the norms guiding journalists providing it, professional identity, and public service. This article argues that these tensions result from a clash between the institutional logic of professional control (Lewis, 2012)) and an ethic of transparency. Drawing from extensive research on a political press corps, involving observation, interviews, and analysis of tweets, this study witnesses the adoption of Twitter in the everyday working practices of reporters. It thereby also provides reasons why Twitter has been so successful in journalism. Tensions between professional control and transparency in journalism may, furthermore, be emblematic for divides in other professions today.     

To be rational or sensitive? The gender difference in how textual environment cue and personal characteristics influence the sentiment expression on social media

Online sentiments expressed by users play critical roles in various social media-based applications, and thus understanding the mechanism of what determines users expressing sentiment with different polarities bears strategic importance. Based on the affective response model (ARM), we develop a conceptual model about the determinants of users’ online sentiment polarity, from the cues of the textual environment from the target tweet and the user’s personal characteristics. Furthermore, the role of gender difference in these effects is also included. Empirical results indicated that users with higher social interactivity and positive historical sentiment expression are more likely to express positive sentiment towards online tweets with higher positive sentiment intensity. Females are more sensitive to the cue of textual environment, i.e, sentiment intensity, in the target tweets when expressing sentiments, while males are more rational when expressing online sentiment than females. Our study supplements the existing study on users’ online interaction behavior as rational and affective action by introducing a new way to study the driving behavior of sentiment expression.     

基于能量优化的微博用户转发行为预测

微博用户转发行为预测是微博社交网络消息扩散模型构建的基础,在舆情监控、市场营销与政治选举等领域有着广泛的应用.为了提高用户转发行为预测的精度,本文在MRF（Markov Random Field）能量优化框架下综合分析了用户属性与微博内容特征、用户转发行为约束、群体转发先验等因素对用户转发行为的影响,并在逻辑回归模型的基础上构造了相应的能量函数对用户转发行为进行了全局性的预测.实验结果表明,微博用户转发行为不仅取决于用户属性、微博内容等特征,而且也受到用户转发行为约束、群体转发先验等因素不同程度的影响.相对于传统算法,本文算法可以更准确地对用户转发行为进行建模,因而可获得更好的预测结果.     

Analytic‐based product opinion detection algorithm for twitter microblogging network

Twitter, with an ever‐increasing user base, has greatly influenced the opinion and purchase habits of the common masses. This has in turn forced the product firms to get involved with sentiment analysis which enables them to mine the actual opinion about their product and make business decisions accordingly. Even though a majority of the existing methods detect sentiment of the tweet with a reasonable accuracy, few ignore emoticons while others consider them as stop words. Emoticons have enabled the users to express their emotion more accurately which eliminates the ambiguity that can arise with usage of words. The trending popularity of emoticons among the users combined with its ease of usage makes it highly lucrative in sentiment analysis. Hence, mining the product opinion without considering the emoticons will severely undermine the accuracy and reliability of the opinion. Moreover, sarcasm detection is still an uncharted territory in opinion mining and is exceedingly difficult to factor it in. Sarcastic tweets when left undetected will affect the accuracy of the opinion. Therefore, the polarity of the individual words and emoticons of the tweets are computed using linguistic analysis. The sarcastic tweets are then classified and eliminated based on their anomalous polarity. By placing a higher emphasis on emoticons, the proposed emoticon‐based linguistic opinion algorithm yields satisfactory results when compared with other traditional and state of the art approaches.     

When retweets persuade: The persuasive effects of dialogic retweeting and the role of social presence in organizations’ Twitter-based communication

Research has shown that organizations tend to use Twitter primarily in a one-way, monologic manner and fall short of using the platform’s technological affordances to engage the public in dialogue. At the same time, relatively little research has addressed the specific persuasive outcomes that organizations could accrue by using Twitter to communicate with the public in a more dialogic way. We investigated the persuasive effect of an organization’s dialogic retweeting (conceptualized as retweeting of user mentions addressed to the organization) by drawing on the concept of social presence and the theory of reasoned action. In an online experiment conducted with an adult sample of U.S. Twitter users, participants were randomly assigned to view either a fictitious organization’s dialogic retweets or the same organization’s monologic tweets of identical content. We found that the dialogic retweets, when compared to the monologic tweets from the organization, induced a higher level of social presence, which, in turn, led to a higher level of subjective norms, more favorable attitudes toward the behavior advocated by the organization in the messages, and greater intention to adopt the behavior. Theoretical and practical implications of these findings are discussed.     

Absorbability,applicability and availability in nursing and care robots: A thematic analysis of Twitter postings

Nursing and care robots (NCR) have become an important technological innovation in various areas in the medical discipline. Previous studies have found that implementation of robots in healthcare is both associated with positive and negative attitudes. This study aims to improve the understanding of the general public’s communication about nursing and care robots through analyzing the content of posts in social media. An advanced social intelligence platform was used to mine Twitter content. From the platform, data were collected historically. An archival and cross-sectional observational study was conducted online. The data set comprising of 5954 tweets were thematically analyzed. Tweets under the theme of absorbability show that nursing and care robots are considered to be a part of users’ lives, either now or sometime in the future for Twitter users, and the topic is tackled as a fact but with humor, skepticism and enthusiasm. Tweets falling under applicability show that potential nursing and care robots usage covers a range of arenas in everyday life. Results thematized as availability show sincere concern about how the accessibility of nursing and care robots in everyday life will affect costs and other economic aspects, both on a global and an individual level as well as on micro and macro levels of economies. Twitter offers a window into attitudes and ideas as well as fundamental beliefs and practices. Thus, monitoring Twitter discussions on social media can provide valuable insights into current attitudes as well as forecasting coming trends. The data includes information about Twitter users’ anxious relationships with nursing and care robots. We raise important questions about the nature of nursing and care robots and their implementations, both in health care but also in everyday living.     

Leveling the playing field: The use of Twitter by politicians during the 2014 Indian general election campaign

In this study, it is theorized that the communicative affordances offered by social media platforms will enable politically under-resourced candidates to contest the marginalization they face in traditional media. Multivariate analyses were conducted of the tweets of 205 political candidates of the 2014 Indian general election. Findings reveal that fringe party candidates received the least media attention and tended to use Twitter more frequently than major party candidates, especially for interaction and mobilization. Minor party candidates also received less media attention, albeit their Twitter usage patterns were not significantly different than major party candidates. The results illustrate that social media platforms can help overcome resource inequality in politics. The larger implications of this study are discussed.     

近红外光谱分析中建模样品优选方法的研究

结合牛奶成分近红外光谱测量系统的实例,在已定的浓度范围内针对牛奶中脂肪、蛋白质、乳糖三成分采用正交设计法优选参与建模的样品.研究中首次利用正交表的\     

微博中基于多关系网络的话题影响力个体挖掘

In micro-blogging contexts such as Twitter, the number of content producers can easily reach tens of thousands, and many users can participate in discussion of any given topic. While many users can introduce diversity, as not all users are equally influential, it makes it challenging to identify the true influencers, who are generally rated as being interesting and authoritative on a given topic. In this study, the influence of users is measured by performing random walks of the multi-relational data in micro-blogging: ret-weet, reply, reintroduce, and read. Due to the uncertainty of the reintroduce and read opera-tions, a new method is proposed to determine the transition probabilities of uncertain relational networks. Moreover, we propose a method for performing the combined random walks for the multi-relational influence network, considering both the transition prob-abilities for intra- and inter-networking. Ex-periments were conducted on a real Twitter dataset containing about 260 000 users and 2.7 million tweets, and the results show that our method is more effective than Twitter-Rank and other methods used to discover influencers.     

Identification of Key Nodes in Microblog Networks

A microblog is a service typically offered by online social networks, such as Twitter and Facebook. From the perspective of information dissemination, we define the concept behind a spreading matrix. A new WeiboRank algorithm for identification of key nodes in microblog networks is proposed, taking into account parameters such as a user's direct appeal, a user's influence region, and a user's global influence power. To investigate how measures for ranking influential users in a network correlate, we compare the relative influence ranks of the top 20 microblog users of a university network. The proposed algorithm is compared with other algorithms — PageRank, Betweeness Centrality, Closeness Centrality, Out‐degree — using a new tweets propagation model — the Ignorants‐Spreaders‐Rejecters model. Comparison results show that key nodes obtained from the WeiboRank algorithm have a wider transmission range and better influence.