Digital Mirror uses linguistic analysis to identify emails containing the most language indicating stress. The topics of those emails are identified. The combination of people and topics that yields the greatest amount of stressful discussion over time is selected for display.

Stress is often shared by people or across topics. When something is stressful, it is normal for many people to be caught up in the stress: this can cause the cells for several people who are discussing that topic to light up at the same time. Similarly, when a person is stressed about one topic, this stress often flows over to other topics. This can result in the cells for several topics to light up at the same time for that person.

It is possible for communications about a single topic to contain multiple emotions. A person may be angry, confused and suspicious all at the same time, for example. When Digital Mirror detects multiple emotions, it signals these by showing more than one emoticon.

Digital Mirror uses linguistic analysis to detect people with whom you have exchanged emails with the most language indicating stress. In this version of Digital Mirror, we select no more than five individuals and five topics for display. If someone does not appear it is probably because, compared with others, this person did not exchange many emails with you containing language indicating stress.

Parts of the timeline at the top of the visualization may be colored yellow. This means that no data from the indicated period was used in this visualization. This version of Digital Mirror shows only a limited amount of information, so it is quite possible relevant data was found for this time, but is not being shown. (The timeline animation runs at higher speed through the yellow-colored regions.)

If you are running Digital Mirror on personal data, please note that this version is optimized for analysis of data in a work or other organizational context. For personal data, it is possible that you may find some of the results less interesting or relevant.

Even if that is not the case, people are sometimes puzzled by the results Digital Mirror presents. But these results are firmly based on the data: if you see something unexpected, it is likely to be because your data suggests something different than your perception. We strongly recommend that you look into the data yourself. In many cases, you can do this by clicking on the specific result in the visualization to bring up an evidence panel that shows some examples that led to the conclusion. In our experience, in the vast majority of cases, the data will support the Digital Mirror result. The challenge is to see yourself as Digital Mirror and others see you. If you have tried and still believe the result is wrong, please let us know.

The detection starts with analysis of the language used in emails, but it is a complex process (one that linguists have been working on for years). Some facets of language that Digital Mirror takes into account are:

• Different emotions: anger versus hope, for example. Like colors, emotions can blend into one another: worry can blend into anxiety which can turn to fear. One broad categorization is to distinguish positive emotions (joy, happiness) from negative ones (sorrow, sadness).
• Time scale: is an emotion momentary or does it continue for a long time?
• Intensity: people can experience different degrees of the same emotion: a little worried versus very, very worried.
• Causality: does an event cause an emotion (fear caused by danger) or does an emotion give rise to an action (secrecy stemming from fear)?

Digital Mirror tends to focus on negative emotions or stressful topics, since these are more generally associated with interesting events. (To quote Tolstoy: Happy families are all alike; each unhappy family is unhappy in its own way.) Digital Mirror distinguishes derogatory language from simple cursing, even when derogatory comments often include cursing.

Sometimes, Digital Mirror distinguishes between causes ("problems") and effects ("worried"); don't be surprised if one label blends over into another. In other cases, Digital Mirror treat causes (ignorance) and effects (confusion) under a common umbrella ("confused"), so you may occasionally find causes mixed up with effects or excuses mixed up with facts.

Digital Mirror is designed to create a reasonably wide range of visualizations, not all of which will be applicable to everyone. It is quite likely that, for any individual user, there will not be enough relevant data for every single visualization. This is normal, which is why we provide a sample for you to enjoy!