Comparison of Public Relations and News Professionals’ Usage of Social Media in Communication Relationships

Professionals in the areas of public relations and news adopt social media extensively in their respective disciplines. Thus the focus of this study is to ascertain how social media was used in professional communication. A questionnaire sent to both journalists and public relations practitioners was distributed via email. The questions focused on professional usage (minutes and hours), identifying both professional and/or social usage. The respondents evaluated Facebook, email, texting, Twitter, FourSquare, LinkedIn, blogs and other internet sites for the following qualities: trust, value, reliability, density of information, and usefulness of information. There was special attention given to social media preference in sending versus receiving communication. The final analysis stressed the respondent\u27s identification of the strengths of social media for professional reasons. This questionnaire (Likert Scale) stressed postmodern perspectives through the use of ethical dimensions as integrated by the views of respondents. For the news media, regardless of age, e-mail was preferred and these reporters did not integrate Facebook into communication. The public relations practitioners’ preferences were split more evenly by age with those under 45 preferring social media

Exploring the Role of Email, Blackboard, and Facebook in Student-Instructor Interactions Outside of Class: A Mixed Methods Study

This dissertation was a mixed methods triangulation design combining quantitative and qualitative components. The purpose of this study was twofold. First, it examined the association between the frequency and quality of students’ online interactions with instructors and the quality of student-instructor relationship. Second, this study explored the meanings of student-instructor interactions mediated by online tools. Quantitative data were collected via an online survey from 320 undergraduate students enrolled at a public research university. Qualitative data sources were in-depth interviews with six undergraduate students and six professors, observations of student-instructor interactions on Facebook, and artifacts of student-instructor interaction via email. Hierarchical regression analysis showed that approximately one third of the variance in student-instructor connectedness was explained by the frequency of and satisfaction with face-to-face, email, Blackboard, and Facebook; the grade obtained in the class; and demographic variables. Significant predictors of connectedness were grade, frequency of face-to-face student interest-driven communication, satisfaction with the face-to-face interactions, and satisfaction with the email communication. The qualitative findings revealed that instructors held expectations of formal communication for email interactions, while students had expectations for response from instructors within one-two business days. The email practices identified for instructors included responding to student email within two days; compensating for limited face-to-face time; engaging students in communication about the class content; and dealing with student disengagement. Students adopted two main practices related to email: avoiding “emergency” emails to contact instructors, and using email to avoid face-to-face contact in some situations. For Facebook interactions, instructors expected that students initiate connections, while students expected that instructors signal their availability for connection with students. Instructors’ Facebook practices pointed out different approaches for accepting student friend requests; and performing interactions. Students’ practices on Facebook highlighted two patterns: initiating connections with instructors during the semester versus at the beginning of the semester. In addition, preserving connections beyond the boundaries of a class was a practice common to students and instructors

The Linguistics of Keyboard-to-screen Communication: A New Terminological Framework

New forms of communication that have recently developed in the context of Web 2.0 make it necessary to reconsider some of the analytical tools of linguistic analysis. In the context of keyboard-to-screen communication (KSC), as we shall call it, a range of old dichotomies have become blurred or cease to be useful altogether, e. g. "asynchronous" versus "synchronous", "written" versus "spoken", "monologic" versus "dialogic", and in particular "text" versus "utterance". We propose alternative terminologies ("communicative act" and "communicative act sequence") that are more adequate to describe the new realities of online communication and can usefully be applied to such diverse entities as weblog entries, tweets, status updates on social network sites, comments on other postings and to sequences of such entities. Furthermore, in the context of social network sites, different forms of communication traditionally separated (i. e. blog, chat, email and so on) seem to converge. We illustrate and discuss these phenomena with data from Twitter and Facebook

Analyzing Social and Stylometric Features to Identify Spear phishing Emails

Spear phishing is a complex targeted attack in which, an attacker harvests information about the victim prior to the attack. This information is then used to create sophisticated, genuine-looking attack vectors, drawing the victim to compromise confidential information. What makes spear phishing different, and more powerful than normal phishing, is this contextual information about the victim. Online social media services can be one such source for gathering vital information about an individual. In this paper, we characterize and examine a true positive dataset of spear phishing, spam, and normal phishing emails from Symantec's enterprise email scanning service. We then present a model to detect spear phishing emails sent to employees of 14 international organizations, by using social features extracted from LinkedIn. Our dataset consists of 4,742 targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack emails sent to 5,912 non victims; and publicly available information from their LinkedIn profiles. We applied various machine learning algorithms to this labeled data, and achieved an overall maximum accuracy of 97.76% in identifying spear phishing emails. We used a combination of social features from LinkedIn profiles, and stylometric features extracted from email subjects, bodies, and attachments. However, we achieved a slightly better accuracy of 98.28% without the social features. Our analysis revealed that social features extracted from LinkedIn do not help in identifying spear phishing emails. To the best of our knowledge, this is one of the first attempts to make use of a combination of stylometric features extracted from emails, and social features extracted from an online social network to detect targeted spear phishing emails.Comment: Detection of spear phishing using social media feature

Quantifying Biases in Online Information Exposure

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this paper, we mine a massive dataset of Web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used Web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."Comment: 25 pages, 10 figures, to appear in the Journal of the Association for Information Science and Technology (JASIST

The Use of Web-Based Support Groups Versus Usual Quit-Smoking Care for Men and Women Aged 21-59 Years: Protocol for a Randomized Controlled Trial (Preprint)

BACKGROUND Existing smoking cessation treatments are challenged by low engagement and high relapse rates, suggesting the need for more innovative, accessible, and interactive treatment strategies. Twitter is a Web-based platform that allows people to communicate with each other throughout the day using their phone. OBJECTIVE This study aims to leverage the social media platform of Twitter for fostering peer-to-peer support to decrease relapse with quitting smoking. Furthermore, the study will compare the effects of coed versus women-only groups on women’s success with quitting smoking. METHODS The study design is a Web-based, three-arm randomized controlled trial with two treatment arms (a coed or women-only Twitter support group) and a control arm. Participants are recruited online and are randomized to one of the conditions. All participants will receive 8 weeks of combination nicotine replacement therapy (patches plus their choice of gum or lozenges), serial emails with links to Smokefree.gov quit guides, and instructions to record their quit date online (and to quit smoking on that date) on a date falling within a week of initiation of the study. Participants randomized to a treatment arm are placed in a fully automated Twitter support group (coed or women-only), paired with a buddy (matched on age, gender, location, and education), and encouraged to communicate with the group and buddy via daily tweeted discussion topics and daily automated feedback texts (a positive tweet if they tweet and an encouraging tweet if they miss tweeting). Recruited online from across the continental United States, the sample consists of 215 male and 745 female current cigarette smokers wanting to quit, aged between 21 and 59 years. Self-assessed follow-up surveys are completed online at 1, 3, and 6 months after the date they selected to quit smoking, with salivary cotinine validation at 3 and 6 months. The primary outcome is sustained biochemically confirmed abstinence at the 6-month follow-up. RESULTS From November 2016 to September 2018, 960 participants in 36 groups were recruited for the randomized controlled trial, in addition to 20 participants in an initial pilot group. Data analysis will commence soon for the randomized controlled trial based on data from 896 of the 960 participants (93.3%), with 56 participants lost to follow-up and 8 dropouts. CONCLUSIONS This study combines the mobile platform of Twitter with a support group for quitting smoking. Findings will inform the efficacy of virtual peer-to-peer support groups for quitting smoking and potentially elucidate gender differences in quit rates found in prior research. CLINICALTRIAL ClinicalTrials.gov NCT02823028; https://clinicaltrials.gov/ct2/show/NCT0282302

Mobile - First News: How People Use Smartphones to Access Information

This report is based on a research study conducted with Nielsen and commissioned by Knight Foundation to explore how people use mobile platforms for news

Measuring Online Social Bubbles

Social media have quickly become a prevalent channel to access information, spread ideas, and influence opinions. However, it has been suggested that social and algorithmic filtering may cause exposure to less diverse points of view, and even foster polarization and misinformation. Here we explore and validate this hypothesis quantitatively for the first time, at the collective and individual levels, by mining three massive datasets of web traffic, search logs, and Twitter posts. Our analysis shows that collectively, people access information from a significantly narrower spectrum of sources through social media and email, compared to search. The significance of this finding for individual exposure is revealed by investigating the relationship between the diversity of information sources experienced by users at the collective and individual level. There is a strong correlation between collective and individual diversity, supporting the notion that when we use social media we find ourselves inside "social bubbles". Our results could lead to a deeper understanding of how technology biases our exposure to new information

Network Sampling: From Static to Streaming Graphs

Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete understanding of network sampling is critical to support the field of network science. In this paper, we outline a framework for the general problem of network sampling, by highlighting the different objectives, population and units of interest, and classes of network sampling methods. In addition, we propose a spectrum of computational models for network sampling methods, ranging from the traditionally studied model based on the assumption of a static domain to a more challenging model that is appropriate for streaming domains. We design a family of sampling methods based on the concept of graph induction that generalize across the full spectrum of computational models (from static to streaming) while efficiently preserving many of the topological properties of the input graphs. Furthermore, we demonstrate how traditional static sampling algorithms can be modified for graph streams for each of the three main classes of sampling methods: node, edge, and topology-based sampling. Our experimental results indicate that our proposed family of sampling methods more accurately preserves the underlying properties of the graph for both static and streaming graphs. Finally, we study the impact of network sampling algorithms on the parameter estimation and performance evaluation of relational classification algorithms