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.     

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.     

Higher education scholars' participation and practices on Twitter

Scholars participate in online social networks for professional purposes. In such networks, learning takes the form of participation and identity formation through engagement in and contribution to networked practices. While current literature describes the possible benefits of online participation, empirical research on scholars' use of online social networks in the educational technology literature is negligible. The purpose of this paper is to understand scholars' naturalistic practices in social networks in general, and on Twitter in particular. Tweets from 45 scholars were analysed qualitatively to arrive at dominant themes describing online social network practice. Findings indicate that scholars participating on Twitter (1) shared information, resources, and media relating to their professional practice; (2) shared information about their classroom and their students; (3) requested assistance from and offered suggestions to others; (4) engaged in social commentary; (5) engaged in digital identity and impression management; (6) sought to network and make connections with others; and (7) highlighted their participation in online networks other than Twitter. These findings assist the field in understanding the emerging practice of scholarly participation in online networks.     

The impact of engagement with social networking sites (SNSs) on cognitive skills

The aim of the present study was to investigate the effect of social networking sites (SNSs) engagement on cognitive and social skills. We investigated the use of Facebook, Twitter, and YouTube in a group of young adults and tested their working memory, attentional skills, and reported levels of social connectedness. Results showed that certain activities in Facebook (such as checking friends’ status updates) and YouTube (telling a friend to watch a video) predicted working memory test performance. The findings also indicated that Active and Passive SNS users had qualitatively different profiles of attentional control. The Active SNS users were more accurate and had fewer misses of the target stimuli in the first block of trials. They also did not discriminate their attentional resources exclusively to the target stimuli and were less likely to ignore distractor stimuli. Their engagement with SNS appeared to be exploratory and they assigned similar weight to incoming streams of information. With respect to social connectedness, participants’ self-reports were significantly related to Facebook use, but not Twitter or YouTube use, possibly as the result of greater opportunity to share personal content in the former SNS.