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Emailing does not preclude emotional exchange and many times it causes us to engage in spiralling exchanges of increasingly angry emailing. The purpose of this chapter is threefold: to explore how factors of temporality are related to anger when emailing, to model circumstances that protect against, but also ignite, anger escalation, and to raise a discussion for practitioners of how to avoid damaging email communication. By intersecting literature on communication, information systems, psychology and organisational studies, factors leading to an ‘emotional verge’ are identified and summarised in a model showing factors likely to prime, but also protect against, anger escalation.

Extended research efforts have been dedicated to understanding how different aspects of online surveys impact the response rate and quality of collected data. With the hope to yield higher response rates, leading survey software solutions (e.g., SurveyMonkey, Qualtrics, QuestionPro) introduced a new technique of embedding a question from an online survey into invitation emails sent to the respondents. The purpose of this chapter is to examine the impact of embedded questions on survey response rate and quality. The results of two experimental studies show that respondents are more likely to respond to a survey with an embedded question compared to an email without one. No statistically significant differences were discovered regarding data quality that was assessed via survey completion rate, respondent bias, and attention check questions. The chapter provides suggestions and guidelines for increasing online surveys' response rate and quality.

Distinguishing phishing emails from legitimate emails continues to be a difficult task for most individuals. This study aims to investigate the psycholinguistic factors associated with deception in phishing email text and their effect on end-user ability to discriminate phishing emails from legitimate emails.

Email messages and end-user decisions collected from a laboratory phishing study were validated and analyzed using natural language processing methods (Linguistic Inquiry Word Count) and penalized regression models (LASSO and Elastic Net) to determine the linguistic dimensions that attackers may use in phishing emails to deceive end-users and measure the impact of such choices on end-user susceptibility to phishing.

We found that most participants, who played the role of a phisher in the study, chose to deceive their end-user targets by pretending to be a familiar individual and presenting time pressure or deadlines. Results show that use of words conveying certainty (e.g. always, never) and work-related features in the phishing messages predicted higher end-user vulnerability. On the contrary, use of words that convey achievement (e.g. earn, win) or reward (cash, money) in the phishing messages predicted lower end-user vulnerability because such features are usually observed in scam-like messages.

Insights from this research show that analyzing emails for psycholinguistic features associated with computer-mediated deception could be used to fine-tune and improve spam and phishing detection technologies. This research also informs the kinds of phishing attacks that must be prioritized in antiphishing training programs.

Applying natural language processing and statistical modeling methods to analyze results from a laboratory phishing experiment to understand deception from both attacker and end-user is novel. Furthermore, results from this work advance our understanding of the linguistic factors associated with deception in phishing email text and its impact on end-user susceptibility.

The purpose of this study is to evaluate strategies to appraise email correspondence to select significant email for permanent preservation without capturing trivial or personal emails. The strategies were tested on the actual email accounts of selected individuals occupying important roles within an important cultural institution in the Northeastern USA.

Treating this art museum as a case study site, email messages are manually appraised for retention using a rubric. Following the appraisal, strategies for expediting this appraisal process, using what is learned from the manual appraisal process, are explored.

A major finding of this study is that sent mail is almost always significant, although preserving only sent mail, or preserving sent mail in combination with inbox items that have been acted upon (replied to or forwarded), are not sufficient to capture significant correspondence. Rather, a social network approach holds the most promise to accelerate the process of email appraisal.

This study provides empirically grounded strategies for appraising email for permanent retention.

The use of email by employees at the Danwood Group was studied and it was found that the interrupt effect from emails is more than generally believed. Employees allowed themselves to be interrupted almost as frequently as telephone calls and the common reaction to the arrival of an email is to react almost as quickly as they would respond to telephone calls. This means the interrupt effect is comparable with that of a telephone call. The recovery time from an email interruption was found to be significantly less than the published recovery time for telephone calls. It is to be concluded, therefore, that while Email is still less disruptive than the telephone, the way the majority of users handle their incoming email has been shown to give far more interruption than expected. By analysing the data captured the authors have been able to create recommendations for a set of guidelines for email usage within the workplace that will increase employee efficiency by reducing the prominence of interruptions, restricting the use of email‐to‐all messages, setting‐up the email application to display three lines of the email and to check for email less frequently. It is recommended that training should be given to staff on how to use email more effectively to increase employee productivity.

Email communication has been used for many years, and has begun to replace traditional, physical correspondence more and more. Compared to a traditional postal service, email services are easier, faster, and free of charge. Standard email, however, is, from a security point of view, more comparable to post cards than letters. Some security techniques and services exist, but few people use them due to lack of awareness, low usability, and a lack of understanding of Public Key Infrastructures (PKIs). A comprehensive comparison is missing, which makes it difficult for users to decide which email service to use. The purpose of this paper is to identify evaluation criteria covering security, usability, and interoperability aspects of email, and to apply them to existing email services.

The authors first define criteria based on literature review, threat analysis and expert consultation. These criteria are then applied, when applicable, to existing approaches including DKIM, SPF, PGP, S/MIME and Opportunistic Encryption, and to common secure email providers including Gmail, Hushmail, and De‐Mail.

None of the existing analysed services meets all the derived criteria. Based on the result of the application of these criteria and the corresponding comparison, the authors propose future directions for usable secure email communication.

The criteria proposed are original and allow an evaluation and a comparison of different email systems that not only considers security aspects, but also the relation and trade‐offs between security, usability and interoperability. Moreover, the trust assumptions involved are also considered.

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost).

Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method.

Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails.

This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study.

In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers.

A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

This study aims to examine the effect of cybersecurity threat and efficacy upon click-through, response to a phishing attack: persuasion and protection motivation in an organizational context.

In a simulated field trial conducted in a financial institute, via PhishMe, employees were randomly sent one of five possible emails using a set persuasion strategy. Participants were then invited to complete an online survey to identify possible protective factors associated with clicking and reporting behavior (N = 2,918). The items of interest included perceived threat severity, threat susceptibility, response efficacy and personal efficacy.

The results indicate that response behaviors vary significantly across different persuasion strategies. Perceptions of threat susceptibility increased the likelihood of reporting behavior beyond clicking behavior. Threat susceptibility and organizational response efficacy were also associated with increased odds of not responding to the simulated phishing email attack.

This study again highlights human susceptibility to phishing attacks in the presence of social engineering strategies. The results suggest heightened awareness of phishing threats and responsibility to personal cybersecurity are key to ensuring secure business environments.

The authors extend existing phishing literature by investigating not only click-through behavior, but also no-response and reporting behaviors. Furthermore, the authors observed the relative effectiveness of persuasion strategies used in phishing emails as they compete to manipulate unsafe email behavior.

The email is an important medium for sharing information rapidly. However, spam, being a nuisance in such communication, motivates the building of a robust filtering system with high classification accuracy and good sensitivity towards false positives. In that context, this paper aims to present a combined classifier technique using a committee selection mechanism where the main objective is to identify a set of classifiers so that their individual decisions can be combined by a committee selection procedure for accurate detection of spam.

For training and testing of the relevant machine learning classifiers, text mining approaches are used in this research. Three data sets (Enron, SpamAssassin and LingSpam) have been used to test the classifiers. Initially, pre-processing is performed to extract the features associated with the email files. In the next step, the extracted features are taken through a dimensionality reduction method where non-informative features are removed. Subsequently, an informative feature subset is selected using genetic feature search. Thereafter, the proposed classifiers are tested on those informative features and the results compared with those of other classifiers.

For building the proposed combined classifier, three different studies have been performed. The first study identifies the effect of boosting algorithms on two probabilistic classifiers: Bayesian and Naïve Bayes. In that study, AdaBoost has been found to be the best algorithm for performance boosting. The second study was on the effect of different Kernel functions on support vector machine (SVM) classifier, where SVM with normalized polynomial (NP) kernel was observed to be the best. The last study was on combining classifiers with committee selection where the committee members were the best classifiers identified by the first study i.e. Bayesian and Naïve bays with AdaBoost, and the committee president was selected from the second study i.e. SVM with NP kernel. Results show that combining of the identified classifiers to form a committee machine gives excellent performance accuracy with a low false positive rate.

This research is focused on the classification of email spams written in English language. Only body (text) parts of the emails have been used. Image spam has not been included in this work. We have restricted our work to only emails messages. None of the other types of messages like short message service or multi-media messaging service were a part of this study.

This research proposes a method of dealing with the issues and challenges faced by internet service providers and organizations that use email. The proposed model provides not only better classification accuracy but also a low false positive rate.

The proposed combined classifier is a novel classifier designed for accurate classification of email spam.

To describe how a personal librarian program at a large university developed, has evolved, and continues to function. This paper aims to assist other librarians in developing their own personal librarian programs. It will also assist librarians who are working to connect to students.

To produce this paper, internal documents were reviewed, existing data were investigated, those who assisted in the development of the program were consulted and literature on personal librarian programs was reviewed.

Personal librarian programs can be an efficient way to connect to students and can create awareness about library services, especially without a formal orientation for new students. The personal librarian program discussed here connects the library to a large number of students with little time and effort. Planning is important in developing a working program.

Librarians can use this article to understand how a personal librarians program functions and how it can benefit their libraries. The paper emphasizes revising an existing program to work more effectively and using planning documents and assessment to help an outreach program run smoothly.

This paper details how a personal librarian program was developed and has evolved as well as how the program functions. The value is in the ways in which the program has been revised and has evolved and in the role that planning has taken in creating an effective program.