What makes re-finding information difficult? A study of email re-finding

Re-nding information that has been seen or accessed before is a task which can be relatively straight-forward, but often it can be extremely challenging, time-consuming and frustrating. Little is known, however, about what makes one re-finding task harder or easier than another. We performed a user study to learn about the contextual factors that influence users' perception of task diculty in the context of re-finding email messages. 21 participants were issued re-nding tasks to perform on their own personal collections. The participants' responses to questions about the tasks combined with demographic data and collection statistics for the experimental population provide a rich basis to investigate the variables that can influence the perception of diculty. A logistic regression model was developed to examine the relationships be- tween variables and determine whether any factors were associated with perceived task diculty. The model reveals strong relationships between diculty and the time lapsed since a message was read, remembering when the sought-after email was sent, remembering other recipients of the email, the experience of the user and the user's ling strategy. We discuss what these findings mean for the design of re-nding interfaces and future re-finding research

Email overload in academia

The emergence of email as a viable and inexpensive communication channel has led to its increased presence in the daily lives of professionals. Email has become a ubiquitous tool in a faster paced and more globally connected world. Besides simple notes, professionals now use email to communicate tasks, important personal and organizational announcements, meeting requests, and share documents. As the importance of email has grown, professionals have made the email client a work nerve center. The vast increase in the volume of email and the use of the email client as a multifunctional tool now threatens the productivity gains it once created. Business professionals suffer from email overload which is accompanied by stress and organizational breakdowns. As a result, many organizations have created email free holidays and professionals have declared email bankruptcy. In this thesis the research on email overload is reviewed, analyzed, and extended through a study of email overload in academia. Using surveys and interviews of faculty at a large university, the researcher found that email overload was present in academia. The study also identified participants’ behaviors in performing email triage, managing email and email overload, and the effects of email overload. The researcher was also able to discover characteristics of cyclical email volumes amongst faculty which may have a direct impact on determining methods of email organization and the occurrence of email overload. Additionally, the study identified that faculty have extended their email client ever further by using it as a task and project manager, information manager, workload barometer, and headline aggregator

Identifying the Bounds of an Internet Resource

Systems for retrieving or archiving Internet resources often assume a URL acts as a delimiter for the resource. But there are many situations where Internet resources do not have a one-to-one mapping with URLs. For URLs that point to the first page of a document that has been broken up over multiple pages, users are likely to consider the whole article as the resource, even though it is spread across multiple URLs. Comments, tags, ratings, and advertising might or might not be perceived as part of the resource whether they are retrieved as part of the primary URL or accessed via a link. Understanding what people perceive as part of a resource is necessary prior to developing algorithms to detect and make use of resource boundaries. A pilot study examined how content similarity, URL similarity, and the combination of the two matched human expectations. This pilot study showed that more nuanced techniques were needed that took into account the particular content and context of the resource and related content. Based on the lessons from the pilot study, a study was performed focused on two research questions: (1) how particular relationships between the content of pages effect expectations and (2) how encountered implementations of saving and perceptions of content value relate to the notion of internet resource bounds. Results showed that human expectations are affected by expected relationships, such as two web pages showing parts of the same news article. They are also affected when two content elements are part of the same set of content, as is the case when two photos are presented as members of the same collection or presentation. Expectations were also affected by the role of the content – advertisements presented alongside articles or photos were less likely to be considered as part of a resource. The exploration of web resource boundaries found that people’s assessments of resource bounds rely on understanding relationships between content fragments on the same web page and between content fragments on different web pages. These results were in the context of personal archiving scenarios. Would institutional archives have different expectations? A follow-on study gathered perceptions in the context of institutional archiving questions to explore whether such perceptions change based on whether the archive is for personal use or is institutional in nature. Results show that there are similar expectations for preserving continuations of the main content in personal and institutional archiving scenarios. Institutional archives are more likely to be expected to preserve the context of the main content, such as additional linked content, advertisements, and author information. This implies alternative resource bounds based on the type of content, relationships between content elements, and the type of archive in consideration. Based on the predictive features that gathered, an automatic classification for determining if two pieces of content should be considered as part of the same resource was designed. This classifier is an example of taking into account the features identified as important in the studies of human perceptions when developing techniques that bound materials captured during the archiving of online resources

Quality Versus Quantity: E-Mail-Centric Task Management and Its Relation With Overload

It is widely acknowledged that many professionals suffer from “e-mail overload.” This articlepresents findings fromin-depth fieldwork that examined thisphenome-non, uncovering six key challenges of task management in e-mail. Analysis of quali-tativeandquantitativedata suggests that it is not simply thequantitybut also thecol

Electronic Communication for Professionals—Challenges and Opportunities

abstract: The 21st-century professional or knowledge worker spends much of the working day engaging others through electronic communication. The modes of communication available to knowledge workers have rapidly increased due to computerized technology advances: conference and video calls, instant messaging, e-mail, social media, podcasts, audio books, webinars, and much more. Professionals who think for a living express feelings of stress about their ability to respond and fear missing critical tasks or information as they attempt to wade through all the electronic communication that floods their inboxes. Although many electronic communication tools compete for the attention of the contemporary knowledge worker, most professionals use an electronic personal information management (PIM) system, more commonly known as an e-mail application and often the ubiquitous Microsoft Outlook program. The aim of this research was to provide knowledge workers with solutions to manage the influx of electronic communication that arrives daily by studying the workers in their working environment. This dissertation represents a quest to understand the current strategies knowledge workers use to manage their e-mail, and if modification of e-mail management strategies can have an impact on productivity and stress levels for these professionals. Today’s knowledge workers rarely work entirely alone, justifying the importance of also exploring methods to improve electronic communications within teams.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Identifying the Bounds of an Internet Resource

Systems for retrieving or archiving Internet resources often assume a URL acts as a delimiter for the resource. But there are many situations where Internet resources do not have a one-to-one mapping with URLs. For URLs that point to the first page of a document that has been broken up over multiple pages, users are likely to consider the whole article as the resource, even though it is spread across multiple URLs. Comments, tags, ratings, and advertising might or might not be perceived as part of the resource whether they are retrieved as part of the primary URL or accessed via a link. Understanding what people perceive as part of a resource is necessary prior to developing algorithms to detect and make use of resource boundaries. A pilot study examined how content similarity, URL similarity, and the combination of the two matched human expectations. This pilot study showed that more nuanced techniques were needed that took into account the particular content and context of the resource and related content. Based on the lessons from the pilot study, a study was performed focused on two research questions: (1) how particular relationships between the content of pages effect expectations and (2) how encountered implementations of saving and perceptions of content value relate to the notion of internet resource bounds. Results showed that human expectations are affected by expected relationships, such as two web pages showing parts of the same news article. They are also affected when two content elements are part of the same set of content, as is the case when two photos are presented as members of the same collection or presentation. Expectations were also affected by the role of the content – advertisements presented alongside articles or photos were less likely to be considered as part of a resource. The exploration of web resource boundaries found that people’s assessments of resource bounds rely on understanding relationships between content fragments on the same web page and between content fragments on different web pages. These results were in the context of personal archiving scenarios. Would institutional archives have different expectations? A follow-on study gathered perceptions in the context of institutional archiving questions to explore whether such perceptions change based on whether the archive is for personal use or is institutional in nature. Results show that there are similar expectations for preserving continuations of the main content in personal and institutional archiving scenarios. Institutional archives are more likely to be expected to preserve the context of the main content, such as additional linked content, advertisements, and author information. This implies alternative resource bounds based on the type of content, relationships between content elements, and the type of archive in consideration. Based on the predictive features that gathered, an automatic classification for determining if two pieces of content should be considered as part of the same resource was designed. This classifier is an example of taking into account the features identified as important in the studies of human perceptions when developing techniques that bound materials captured during the archiving of online resources

Detecting the Intent of Email Using Embeddings, Deep Learning and Transfer Learning

Throughout the years\u27 several strategies and tools were proposed and developed to help the users cope with the problem of email overload, but each of these solutions had its own limitations and, in some cases, contribute to further problems. One major theme that encapsulates many of these solutions is automatically classifying emails into predefined categories (ex: Finance, Sport, Promotion, etc.) then move/tag the incoming email to that particular category. In general, these solutions have two main limitations: 1) they need to adapt to changing user’s behavior. 2) they require handcrafted features engineering which in turn need a lot of time, effort, and domain knowledge to produce acceptable performance.This dissertation aims to explore the email phenomenon and provide a scalable solution that addresses the above limitations. Our proposed system requires no handcrafted features engineering and utilizes the Speech Act Theory to design a classification system that detects whether an email required an action (i.e. to do) or no action (i.e. to read). We can automate both the features extraction and the classification phases by using our own word embeddings, trained on the entire Enron Email dataset, to represent the input. Then, we use a convolutional layer to capture local tri-gram features, followed by an LSTM layer to consider the meaning of a given feature (trigrams) concerning some “memory” of words that could occur much earlier in the email. Our system detects the email intent with 89% accuracy outperforming other related works. In developing this system, we followed the concept of Occam’s razor (i.e. law of parsimony). It is a problem-solving principle stating that entities should not be multiplied without necessity. Chapter four present our efforts to simplify the above-proposed model by dropping the use of the CNN layer and showing that fine-tuning a pre-trained Language Model on the Enron email dataset can achieve comparable results. To the best of our knowledge, this is the first attempt of using transfer learning to develop a deep learning model in the email domain. Finally, we showed that we could even drop the LSTM layer by representing each email’s sentences using contextual word/sentence embeddings. Our experimental results using three different types of embeddings: context-free word embeddings (word2vec and GloVe), contextual word embeddings (ELMo and BERT), and sentence embeddings (DAN-based Universal Sentence Encoder and Transformer-based Universal Sentence Encoder) suggest that using ELMo embeddings produce the best result. We achieved an accuracy of 90.10%, comparing with word2vec (82.02%), BERT (58.08%), DAN-based USE (86.66%), and Transformer-based USE (88.16%)