Many smartphone apps distract us from our feelings–it’s hard to get in touch with crushing sadness while crushing candy. But what if there were an app that made users more aware of their emotions, as well as those of their friends and coworkers, and thus made difficult conversations easier?
Exclusively for Fast Company’s Difficult Conversations week, Boston-based design firm Altitude has created Moodit, a concept app that uses emotion-detecting software to monitor your text messages, social media updates, and newsfeeds to gauge how you and your peers are feeling. It’s geared toward avoiding conflict in that it makes you more aware of the emotions that make conversations difficult. Basically, it’s a robot that reads your mind and tells you to calm down, cheer up, or celebrate accordingly, and to watch out for friends in bad moods.
“For me, there are two major causes of difficult conversations,” Cindy Weflen, a senior engineer at Altitude, tells Co.Design. “One is not being aware of your own emotions during a conversation, or not being aware of the other person’s emotions, not picking up on nuance. That can result in one or both parties getting upset. The other cause is usually being emotional or upset about something and not dealing with it, keeping it bottled up, and then letting it affect other parts of your life.”
Moodit aims to help solve these problems by making users more aware of their own and other’s emotions, using an interface inspired by mood rings, with emotions represented by a spectrum of colors. The app would help you deal with these emotions more effectively by sending you prompts and notifications. Say you’re an angsty teenager and tweet “everything sucks”–the app would send you a push notification saying “Yikes! Things have taken a turn for the bitter in your posts. Is there a difficult conversation or feeling you’re avoiding? Be brave and face it head on!” If you’re in a Pollyannaish mood, tweeting “Sunny day, chasing the clouds away,” it might suggest you “take advantage of your good vibes and spread the cheer. It’s a great time to reach out to someone in your network who’s feeling down.”
The homepage visualizes Moodit users in your social network as user profile photos in rings, rendered in colors that indicate their current mood. “Cindy is content,” it might say, with a calm green ring around Cindy’s photo; while “Jeremy is angry,” with a red ring around Jeremy’s photo. It also aggregates the emotional temperature of entire networks–“Facebook is annoyed,” “Michigan is angry”–to report on “trending” emotions, the way Facebook currently posts trending stories. “Moodit tracks your feelings in real time,” Weflen says. “As you’re texting with a friend, for example, it notes whether you moved from writing in full sentences to short, curt, one-word responses.” A circle around your user photo would turn red accordingly, suggesting you might be getting annoyed, and that you might do well to take a few deep breaths.
The mood detection software the concept would employ exists today–it was used most infamously in Facebook’s controversial news feed manipulation experiment. It relies primarily on a database of words coded as “negative” or “positive” in emotional charge. While it’s still in early stages, and so far uses broad strokes to detect whether emotions are positive or negative, “there’s a lot of work being done in the space to make the software detect nuance,” Weflen says. Microsoft, for example, is working on a software that detects moods on Twitter; and there’s also Tonecheck, a downloadable application that assesses the tone of an email before you hit send.
Moodit differs from existing mood-tracking apps in that it incorporates social media and its use of mood-detection software. “Moodit is meant to be fun–it’s not meant to be a therapy regimen or anything,” Weflen says. Some existing mood-tracking apps, like Moodscope, are used in clinical settings for tracking conditions like depression, by requiring users to fill out more detailed questionnaires about their psychic state. Moodit’s approach allows for minimal effort–you don’t have to enter any data into the app, as it automatically gathers data from your texts and social media.
The concept has a few caveats–to work effectively and accurately, it would require that users are active social media users or texters, and that their posts clue the emotion-detecting software into their personal lives. But in the age of hyper-connected oversharing, that’s pretty much everyone.