TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

Twitter provides search services to help people find users to follow by recommending popular users or the friends of their friends. However, these services neither offer the most relevant users to follow nor provide a way to find the most interesting tweet messages for each user. Recently, collaborative filtering techniques for recommendations based on friend relationships in social networks have been widely investigated. However, since such techniques do not work well when friend relationships are not sufficient, we need to take advantage of as much other information as possible to improve the performance of recommendations.In this paper, we propose TWILITE, a recommendation system for Twitter using probabilistic modeling based on latent Dirichlet allocation which recommends top-K users to follow and top-K tweets to read for a user. Our model can capture the realistic process of posting tweet messages by generalizing an LDA model as well as the process of connecting to friends by utilizing matrix factorization. We next develop an inference algorithm based on the variational EM algorithm for learning model parameters. Based on the estimated model parameters, we also present effective personalized recommendation algorithms to find the users to follow as well as the interesting tweet messages to read. The performance study with real-life data sets confirms the effectiveness of the proposed model and the accuracy of our personalized recommendations.     

Sourcing the Arab Spring: A Case Study of Andy Carvin's Sources on Twitter During the Tunisian and Egyptian Revolutions

News sourcing practices are critical as they shape from whom journalists get their information and what information they obtain, mostly from elite sources. This study evaluates whether social media platforms expand the range of actors involved in the news through a quantitative content analysis of the sources cited by NPR's Andy Carvin on Twitter during the Arab Spring. Results show that, on balance, nonelite sources had a greater representation in the content than elite sources. Alternative actors accounted for nearly half of the messages. The study points to the innovative forms of production that can emerge with new communication technologies, with the journalist as a central node trusted to authenticate and interpret news flows on social awareness streams.     

College students and risk-taking behaviour on Twitter versus Facebook

The propensity of college students to post content that they know may be unacceptable to future employers or other authority figures has been well established. Yet research on this topic has tended to focus exclusively on Facebook, which is problematic for two reasons. First, many young social media users are shifting away from Facebook and towards Twitter and other services. Second, college students have changed their use of social media over time and may now be more cautious about what they post on Facebook. To address this issue, a survey-based field study was conducted to compare student comfort levels with authority figures viewing their Facebook and Twitter accounts. Specifically, undergraduate business students attending a large university in the midwest of the USA were surveyed about their Facebook and Twitter accounts. Findings indicate that college students are markedly less comfortable with authority figures viewing their Twitter accounts. Paradoxically, a great majority of the study respondents were found to have public Twitter accounts, while only a very small minority have public Facebook accounts. This finding suggests that students perceive less risk on Twitter versus Facebook or that they are writing to different imagined audiences on the two platforms. Implications include the need for further inquiry and an awareness of educators and human resources professionals about students’ current social media practices.     

Urban Social Media Demographics: An Exploration of Twitter Use in Major American Cities

This article explores intersections between place, race/ethnicity, and gender amongst American Twitter users and makes an argument that studying the intensity of tweets provides insights into how and why particular groups tweet. Given recent events in American political life such as the shooting in Ferguson, Missouri and the reactions by young, urban African Americans on Twitter, understanding the role of race, place, gender, and age is important. We observed the time between tweets of urban American Twitter users and explored whether the medium may be providing traditionally marginalized groups, such as young Black men, with potential avenues for mobilizing communication and access to resources.     

The Rise of Twitter in the Political Campaign: Searching for Intermedia Agenda‐Setting Effects in the Presidential Primary

Questions exist over the extent to which social media content may bypass, follow, or attract the attention of traditional media. This study sheds light on such dynamics by examining intermedia agenda‐setting effects among the Twitter feeds of the 2012 presidential primary candidates, Twitter feeds of the Republican and Democratic parties, and articles published in the nation's top newspapers. Daily issue frequencies within media were analyzed using time series analysis. A symbiotic relationship was found between agendas in Twitter posts and traditional news, with varying levels of intensity and differential time lags by issue. While traditional media follow candidates on certain topics, on others they are able to predict the political agenda on Twitter.     

基于中间件技术的Twitter系统研究与实现

本文将中间件技术引入到Twitter系统结构中,设计并实现了一套可信任的Twitter解决方案,该方案包括以下几部分:首先将系统的消息类型划分为存储型消息和验证型消息,并针对这两种不同的消息类型提出了相应的处理机制;提出以星型结构为基础的系统架构,引入消息中间件层,用于处理应用服务器传递的信息,采用中间件的分层结构体系,可以平衡各前端应用节点的负载,提高系统的可靠性、可伸缩性和可扩展性,使承载大访问量的Twitter服务成为可能。     

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.     

Stochastic Gradient Boosting Model for Twitter Spam Detection

In today’s world of connectivity there is a huge amount of data than we could imagine. The number of network users are increasing day by day and there are large number of social networks which keeps the users connected all the time. These social networks give the complete independence to the user to post the data either political, commercial or entertainment value. Some data may be sensitive and have a greater impact on the society as a result. The trustworthiness of data is important when it comes to public social networking sites like facebook and twitter. Due to the large user base and its openness there is a huge possibility to spread spam messages in this network. Spam detection is a technique to identify and mark data as a false data value. There are lot of machine learning approaches proposed to detect spam in social networks. The efficiency of any spam detection algorithm is determined by its cost factor and accuracy. Aiming to improve the detection of spam in the social networks this study proposes using statistical based features that are modelled through the supervised boosting approach called Stochastic gradient boosting to evaluate the twitter data sets in the English language. The performance of the proposed model is evaluated using simulation results.

     

Tweet this: A uses and gratifications perspective on how active Twitter use gratifies a need to connect with others

Twitter is an Internet social-network and micro-blogging platform with both mass and interpersonal communication features for sharing 140-character messages, called tweets, with other people, called followers. Hierarchical OLS regression of survey results from 317 Twitter users found that the more months a person is active on Twitter and the more hours per week the person spends on Twitter, the more the person gratifies a need for an informal sense of camaraderie, called connection, with other users. Controlling for demographic variables does not diminish this positive relationship. Additionally, frequency of tweeting and number of @replies, public messages between Twitter users, mediate the relationship between active Twitter use and gratifying a need for connection. Results are discussed in light of uses and gratifications theory.     

Unfriend or ignore tweets?: A time series analysis on Japanese Twitter users suffering from information overload

In recent years, with increased opportunities to post content on social media, a number of users are experiencing information overload in relation to social media use. This study addresses how Japanese Twitter users suffering from information overload cope with their stress, focusing on two actions: (1) The “unfriending” activities and (2) The changes in tweet processing methods. Objective data, such as numbers of friends, were collected through Twitter's open Application Programming Interfaces (APIs), and subjective data, such as perceived information overload and tweet processing methods, were collected through a web-based survey as a panel dataset (n = 778). The results demonstrated that although users experience information overload, they continue to increase their number of friends, and that the users who experience information overload modify their usage habits to avoid seeing all received tweets. In short, users do not choose a strategy to reduce the absolute number of received tweets, but only a strategy that involves changing the processing method of the received tweets.