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Don’t Use The Placeholder Attribute




Don’t Use The Placeholder Attribute

Eric Bailey



Introduced as part of the HTML5 specification, the placeholder attribute “represents a short hint (a word or short phrase) intended to aid the user with data entry when the control has no value. A hint could be a sample value or a brief description of the expected format.”

This seemingly straightforward attribute contains a surprising amount of issues that prevent it from delivering on what it promises. Hopefully, I can convince you to stop using it.

Technically Correct

Inputs are the gates through which nearly all e-commerce has to pass. Regardless of your feelings on the place of empathy in design, unusable inputs leave money on the table.

The presence of a placeholder attribute won’t be flagged by automated accessibility checking software. However, this doesn’t necessarily mean it’s usable. Ultimately, accessibility is about people, not standards, so it is important to think about your interface in terms beyond running through a checklist.

Call it remediation, inclusive design, universal access, whatever. The spirit of all these philosophies boils down to making things that people—all people—can use. Viewed through this lens, placeholder simply doesn’t hold up.

The Problems

Translation

Browsers with auto-translation features such as Chrome skip over attributes when a request to translate the current page is initiated. For many attributes, this is desired behavior, as an updated value may break underlying page logic or structure.

One of the attributes skipped over by browsers is placeholder. Because of this, placeholder content won’t be translated and will remain as the originally authored language.

If a person is requesting a page to be translated, the expectation is that all visible page content will be updated. Placeholders are frequently used to provide important input formatting instructions or are used in place of a more appropriate label element (more on that in a bit). If this content is not updated along with the rest of the translated page, there is a high possibility that a person unfamiliar with the language will not be able to successfully understand and operate the input.

This should be reason enough to not use the attribute.

While we’re on the subject of translation, it’s also worth pointing out that location isn’t the same as language preference. Many people set their devices to use a language that isn’t the official language of the country reported by their browser’s IP address (to say nothing of VPNs), and we should respect that. Make sure to keep your content semantically described—your neighbors will thank you!

Interoperability

Interoperability is the practice of making different systems exchange and understand information. It is a foundational part of both the Internet and assistive technology.

Semantically describing your content makes it interoperable. An interoperable input is created by programmatically associating a label element with it. Labels describe the purpose of an input field, providing the person filling out the form with a prompt that they can take action on. One way to associate a label with an input, is to use the for attribute with a value that matches the input’s id.

Without this for/id pairing, assistive technology will be unable to determine what the input is for. The programmatic association provides an API hook that software such as screen readers or voice recognition can utilize. Without it, people who rely on this specialized software will not be able to read or operate inputs.


A diagram demonstrating how code gets converted into a rendered input, and how the code’s computed properties get read by assistive technology. The code is a text input with a label that reads Your Name. The listed computed properties are the accessible name, which is Your Name, and a role of textbox.


How semantic markup is used for both visual presentation and accessible content. (Large preview)

The reason I am mentioning this is that placeholder is oftentimes used in place of a label element. Although I’m personally baffled by the practice, it seems to have gained traction in the design community. My best guess for its popularity is the geometrically precise grid effect it creates when placed next to other label-less input fields acts like designer catnip.


Facebook’s signup form. A heading reads, “Sign Up. It’s free and always will be.” Placeholders are being used as labels, asking for your first name, last name, mobile number or email, and to create a new password for your account Screenshot.


An example of input grid fetishization from a certain infamous blue website. (Large preview)

The floating label effect, a close cousin to this phenomenon, oftentimes utilizes the placeholder attribute in place of a label, as well.

A neat thing worth pointing out is that if a label is programmatically associated with an input, clicking or tapping on the label text will place focus on the input. This little trick provides an extra area for interacting with the input, which can be beneficial to people with motor control issues. Placeholders acting as labels, as well as floating labels, cannot do that.

Cognition

The 2016 United States Census lists nearly 15 million people who report having cognitive difficulty — and that’s only counting individuals who choose to self-report. Extrapolating from this, we can assume that cognitive accessibility concerns affect a significant amount of the world’s population.

Self-reporting is worth calling out, in that a person may not know, or feel comfortable sharing that they have a cognitive accessibility condition. Unfortunately, there are still a lot of stigmas attached to disclosing this kind of information, as it oftentimes affects things like job and housing prospects.

Cognition can be inhibited situationally, meaning it can very well happen to you. It can be affected by things like multitasking, sleep deprivation, stress, substance abuse, and depression. I might be a bit jaded here, but that sounds a lot like conditions you’ll find at most office jobs.

Recall

The umbrella of cognitive concerns covers conditions such as short-term memory loss, traumatic brain injury, and Attention Deficit Hyperactivity Disorder. They can all affect a person’s ability to recall information.

When a person enters information into an input, its placeholder content will disappear. The only way to restore it is to remove the information entered. This creates an experience where guiding language is removed as soon as the person attempting to fill out the input interacts with it. Not great!

An input called “Your Birthdate” being filled out. The placeholder reads, “MM/DD/YYY” and the animation depicts the person filling it out getting to the year portion and having to delete the text to be able to go back and review what the proper formatting is.
Did they want MM/DD/YY, or MM/DD/YYYY? (Large preview)

When your ability to recall information is inhibited, it makes following these disappearing rules annoying. For inputs with complicated requirements to satisfy—say creating a new password—it transcends annoyance and becomes a difficult barrier to overcome.

An input called “Create a Password” being filled out. The placeholder reads, “8-15 characters, including at least 3 numbers and 1 symbol.” and the animation depicts the person filling it out having to delete the text to be able to go back and review what the password requirements are.
Wait—what’s the minimum length? How many numbers do they want again? (Large preview)

While more technologically-sophisticated people may have learned clever tricks such as cutting entered information, reviewing the placeholder content to refresh their memory, then re-pasting it back in to edit, people who are less technologically literate may not understand why the help content is disappearing or how to bring it back.

Digital Literacy

Considering that more and more of the world’s population is coming online, the onus falls on us as responsible designers and developers to make these people feel welcomed. Your little corner of the Internet (or intranet!) could very well be one of their first experiences online — assuming that the end user “will just know” is simple arrogance.

For US-based readers, a gentle reminder that new may not mean foreign. Access is on the rise for older Americans. While digital literacy will become more commonplace among older populations as time marches on, accessibility issues will as well.

For someone who has never encountered it before, placeholder text may look like entered content, causing them to skip over the input. If it’s a required field, form submission will create a frustrating experience where they may not understand what the error is, or how to fix it. If it’s not a required field, your form still runs the unnecessary risk of failing to collect potentially valuable secondary information.

Utility

Placeholder help content is limited to just a string of static text, and that may not always be sufficient to communicate the message. It may need to have additional styling applied to it, or contain descriptive markup, attributes, images, and iconography.

This is especially handy in mature design systems. The additional styling options created by moving the string of text out of the input element means it can take advantage of the system’s design tokens, and all the benefits that come with using them.

Placeholder text’s length is also limited to the width of the input it is contained in. In our responsive, mobile-first world, there stands a very good chance that important information could be truncated:


An input called Your YAMA Code, with a truncated placeholder that reads, You can find this code on the ba-


I guess I’ll never know where that code is. (Large preview)

Vision

Color Contrast

The major browsers’ default styles for placeholder content use a light gray color to visually communicate that it is a suggestion. Many custom input designs follow this convention by taking the color of input content and lightening it.

Unfortunately, this technique is likely to run afoul of color contrast issues. Color contrast is a ratio determined by comparing the luminosity of the text and background color values; in this case, it’s the color of the placeholder text over the input’s background.

See the Pen Default browser placeholder contrast ratios by Eric Bailey (@ericwbailey) on CodePen.

If the placeholder content has a contrast ratio that is too low to be perceived, it means that information critical to filling out a form successfully may not be able to be seen by people experiencing low vision conditions. For most common input font sizing, the ratio is 4.5:1.

Like all accessibility concerns, low vision conditions can be permanent or temporary, biological or environmental, or a combination. Biological disabilities include conditions like farsightedness, color blindness, dilated pupils, and cataracts. Environmental conditions include circumstances such as the glare of the mid-day sun, a battery-saving low brightness setting, privacy screens, grease and makeup left on your screen by your last phone call, and so on.

This ratio isn’t some personal aesthetic preference that I’m trying to force onto others arbitrarily. It’s part of a set of painstakingly-developed rules that help ensure that the largest possible swath of people can operate digital technology, regardless of their ability or circumstance. Consciously ignoring these rules is to be complicit in practicing exclusion.

And here’s the rub: In trying to make placeholder attributes inclusive, the updated higher contrast placeholder content color may become dark enough to be interpreted as entered input, even by more digitally literate people. This swings the issue back into cognitive concerns land.


The email address field on GoFundMe’s password reset page has a placeholder that reads email@address.com and is set to a dark black color that makes it look like entered input. Screenshot.


The placeholder text color on GoFundMe’s password reset page makes it appear like entered input. Additionally, the checkmark icon on the Request New Password button makes it seem like the request has already been processed. (Large preview)

High Contrast Mode

The Windows operating system contains a feature called High Contrast Mode. When activated, it assigns new colors to interface elements from a special high contrast palette that uses a limited number of color options. Here’s an example of what it may look like:


An input field with a label that reads “Donation amount” and a placeholder that reads “$25.00.” The screenshot is taken with Windows High Contrast mode active, so the placeholder element looks like entered text content. Screenshot.


Windows 10 set to use the High Contrast Mode 1 theme running Internet Explorer 11. (Large preview)

In High Contrast Mode, placeholder content is assigned one of those high contrast colors, making it look like pre-filled information. As discussed earlier, this could prevent people from understanding that the input may need information entered into it.

You may be wondering if it’s possible to update the styling in High Contrast Mode to make a placeholder more understandable. While it is possible to target High Contrast Mode in a media query, I implore you not to do so. Front-end developer Hugo Giraudel said it best:

“High contrast mode is not about design anymore but strict usability. You should aim for highest readability, not color aesthetics.”

The people that rely on High Contrast Mode use it because of how predictable it is. Unduly altering how it presents content may interfere with the only way they can reliably use a computer. In the case of lightening the color of placeholder content to make it appear like its non-High Contrast Mode treatment, you run a very real risk of making it impossible for them to perceive.

A Solution

To recap, the placeholder attribute:

  • Can’t be automatically translated;
  • Is oftentimes used in place of a label, locking out assistive technology;
  • Can hide important information when content is entered;
  • Can be too light-colored to be legible;
  • Has limited styling options;
  • May look like pre-filled information and be skipped over.

Eesh. That’s not great. So what can we do about it?

Design

Move the placeholder content above the input, but below the label:


An input with a label that reads, Your employee ID number, and help content below the label that reads, Can be found on your employee intranet profile. Example: a1234567-89. The example ID has been styled using a monospaced font.


Large preview

This approach:

  • Communicates a visual and structural hierarchy:
    • What this input is for,
    • Things you need to know to use the input successfully, and
    • the input itself.
  • Can be translated.
  • Won’t look like pre-filled information.
  • Can be seen in low vision circumstances.
  • Won’t disappear when content is entered into the input.
  • Can include semantic markup and be styled via CSS.

Additionally, the help content will be kept in view when the input is activated on a device with a software keyboard. If placed below the input, the content may be obscured when an on-screen keyboard appears at the bottom of the device viewport:


iOS’ on-screen keyboard is obscuring information about password requirements on a “Set a password” input. Screenshot.


Content hidden by an on-screen keyboard. 3rd party keyboards with larger heights may have a greater risk of blocking important content. (Large preview)

Development

Here’s how to translate our designed example to code:

<div class="input-wrapper">
  <label for="employee-id">
    Your employee ID number
  </label>
  <p
    id="employee-id-hint"
    class="input-hint">
    Can be found on your employee intranet profile. Example: <samp>a1234567-89</samp>.
  </p>
  <input
    id="employee-id"
    aria-describedby="employee-id-hint"
    name="id-number"
    type="text" />
</div>

This isn’t too much of a departure from a traditional accessible for/id attribute pairing: The label element is programmatically associated with the input via its id declaration of “employee-id”. The p element placed between the label and input elements acts as a replacement for a placeholder attribute.

“So,” you may be wondering. “Why don’t we just put all that placeholder replacement content in the label element? It seems like it’d be a lot less work!” The answer is that developer convenience shouldn’t take priority over user experience.

By using aria-describedby to programmatically associate the input with the p element, we are creating a priority of information for screen readers that has parity with what a person browsing without a screen reader would experience. aria-describedby ensures that the p content will be described last, after the label’s content and the kind of input it is associated with.

In other words, it’s what content the input is asking for, what type of input it is, then additional help if you need it — exactly what someone would experience if they look at form input.

User experience encompasses all users, including those who navigate with the aid of screen readers. The help content is self-contained and easy to navigate to and from, should the person using a screen reader need to re-reference it. As it is a self-contained node, it can also be silenced (typically with the Control key) without risking muting other important information.

Including the help content as part of the label makes it unnecessarily verbose. labels should be meaningful, but also concise. Adding too much information to a label may have the opposite of the desired effect, making it too long to recall or simply too frustrating to listen to all the way through. In fact, the Web Content Accessibility Guidelines has rules that specifically address this: Success Criteria 2.4.6 and 3.3.2.

Example

Here is the solution implemented in live code:

See the Pen Don’t use the placeholder attribute by Eric Bailey (@ericwbailey) on CodePen.

And here’s a video demonstrating how popular screen readers handle it:

A Better Solution

“The less an interface requires of its users, the more accessible it is.”

Alice Boxhall

A final thought: Do you even need that additional placeholder information?

Good front-end solutions take advantage of special input attributes and accommodating validation practices to prevent offloading the extra work onto the person who simply just wants to use your site or app with as little complication as possible.

Good copywriting creates labels that clearly and succinctly describe the input’s purpose. Do a good enough job here and the label cuts through the ambiguity, especially if you test it beforehand.

Good user experience is all about creating intelligent flows that preempt people’s needs, wants, and desires by capitalizing on existing information to remove as many unnecessary questions as possible.

Accommodating the people who use your website or web app means taking a critical eye at what you take for granted when you browse the Internet. By not making assumptions about other people’s circumstances — including the technology they use — you can do your part to help prevent exclusion.

Take some time to review your design and code and see what doesn’t stand up to scrutiny — checking to see if you use the placeholder attribute might be a good place to start.

Standing on the shoulders of giants. Thanks to Roger Johansson, Adam Silver, Scott O’Hara, and Katie Sherwin for their writing on the subject.

Smashing Editorial
(rb, ra, yk, il)


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Don’t Use The Placeholder Attribute

Analyzing Your Company’s Social Media Presence With IBM Watson And Node.js




Analyzing Your Company’s Social Media Presence With IBM Watson And Node.js

If you are unfamiliar with Machine Learning (ML) technology, it has existed in science fiction for many years and is finally reaching its maturity in our society. One of the first ML examples I saw as a kid was in Star Trek’s The Next Generation when Lieutenant Tasha Yar trains with her holographic opponent that learns how to fight and better defeat in future battles.

In today’s society, China has developed a “lane robot” that is a guard rail controlled by a computer system that can direct the flow of traffic into different lanes, increasing safety and improving traveling time. This is done automatically based on time of day and how much traffic is flowing in each direction.

Another example is Pittsburg unveiling AI traffic signals that automatically detect traffic patterns and alter the traffic lights on-the-fly. Each light is controlled independently to help reduce both the commuting time and the idling time of cars. According to the article, pilot tests have demonstrated a reduced travel time of 25% and idling time by over 40%. There are, of course, hundreds of other examples of ML technology that make intelligent decisions based on the content it consumes.

To accomplish today’s goal, I am going to demonstrate (using Node.js) how to perform a search with Twitter’s API to retrieve content that will be inputted into the ML algorithm to be analyzed. This way, you’ll be provided with characteristics about the users who wrote that specific content so that you can get a better understanding of your audience. The example application will be written using Node.js as the server.

It is beyond the scope of this article to demonstrate how to write an ML algorithm. Instead, to aid in the analysis, I will demonstrate how to use IBM’s Watson to help you understand the general personality of your social media audience.

What Is IBM Watson?

In 2011, Watson began as a computer system that attempted to index the (entire) Internet. It was originally programmed to answer questions posed in ordinary English. Watson competed and won on the TV show Jeopardy! claiming a $1,000,000 cash prize.

Watson was now a proven success.

With the fame of winning on Jeopardy!, IBM has continued to push Watson’s capabilities. Watson has evolved into an enterprise-level application that is focused on Artificial Intelligence (AI) which you can train to identify what you care about most allowing you to make smarter decisions automatically.

The suite of Watson’s services is divided into six high-level categories:

  1. Conversation
    The services in this category allow you to build intelligent chatbot’s or a virtual customer service agent.
  2. Knowledge
    This category is focused on teaching Watson how to interpret data to unlock hidden value and monitor trends.
  3. Vision
    This service provides the ability to tag content inside an image that is used to train Watson to be able to automatically recognize the same pattern inside of other images.
  4. Speech
    These services provide the ability to convert speech to text and the inverse, text to speech.
  5. Language
    This category is split between translating one language to another as well as interpreting the text to predict what predefined category the text belongs to.
  6. Empathy
    This category is devoted to understanding the content’s tone, personality, and emotional state. Inside this category is a service called “Personality Insights” that will be used in this article to predict the personality characteristics with the social media content we will provide it.

This article will be focusing on understanding the personality of the content that we will fetch from Twitter. However, as you can see, Watson provides many other AI features that you can explore to automate many other processes simply through training and content aggregation.

Personality Insights

Personality Insights will analyze content and help you understand the habits and preferences at an individual level and at scale. This is called the ‘personality profile.’ The profile is split into two high-level groups: Personality characteristics and Consumption preferences. These groups are further broken down into more finite components.

Note: To help understand the high-level concepts (before we deep dive into the results), the Personality Insights documentation provides this helpful summary describing how the profile is inferred from the content you provide it.


IBM Watson’s Big Five Personality Traits


Big Five Personality Traits. Image courtesy: IBM.com. (Large preview)

Personality Characteristics

The Personality Insights service infers personality characteristics based on three primary models:

  • The ‘Big Five’ personality characteristics represent the most widely used model for generally describing how a person engages with the world. The model includes five primary dimensions:
    • Agreeableness
    • Conscientiousness
    • Extraversion
    • Emotional range
    • Openness
      Note: Each dimension has six facets that further characterize an individual according to the dimension.
  • Needs describe which aspects of a product will resonate with a person. The model includes twelve characteristic needs:
    • Excitement
    • Harmony
    • Curiosity
    • Ideal
    • Closeness
    • Self-expression
    • Liberty
    • Love
    • Practicality
    • Stability
    • Challenge
    • Structure
  • Values describe motivating factors that influence a person’s decision making. The model includes five values:
    • Self-transcendence / Helping others
    • Conservation / Tradition
    • Hedonism / Taking pleasure in life
    • Self-enhancement / Achieving success
    • Open to change / Excitement

For more information, see Personality models.

Consumption preferences

Based on the personality characteristics inferred from the input text, the service can also return an indication of the author’s consumption preferences. ‘Consumption preferences’ indicate the author’s likelihood to pursue different products, services, and activities. The service groups the individual preferences into eight categories:

  • Shopping
  • Music
  • Movies
  • Reading and learning
  • Health and activity
  • Volunteering
  • Environmental concern
  • Entrepreneurship

Each category contains from one to as many as a dozen individual preferences.

Note: For more information, see Consumption preferences. For a more in-depth overview of a particular point of interest, I suggest you refer to the Personality Insights documentation.

To be effective, Watson requires a minimum of a hundred words to provide an insight into the consumer’s personality. The more words provided, the better Watson can analyze and determine the consumer’s preference.

This means, if you wish to target individuals, you will need to collect more data than one or two tweets from a specific person. However, if a user writes a product review, blog post, email, or anything else related to your company, this could be analyzed on both an individual level and at scale.

To begin, let’s start by setting up the Personality Insights service to begin analyzing a real-world example.

Configuring The Personality Insights Service

Watson is an enterprise application but they offer a free, limited service. Once you’ve created an account and are logged in, you will need to add the Personality Insight service. IBM offers a Lite plan that is free. The Lite plan is limited to 1,000 API calls per month and is automatically deleted after 30 days — perfect for our demonstration.


Create the Personality Insights Service


Create the Personality Insights Service. (Large preview)

Once the service has been added, we will need to retrieve the service’s credentials to perform API calls against it. From Watson’s Dashboard, your service should be displayed. After you’ve selected the service, you’ll find a link to view the Service credentials in the left-hand menu. You will need to create a new ‘Credential.’ A unique name is required and optional configuration parameters can be defaulted for this login. For now, we will leave the configuration options empty.

After you have created a credential, select the ‘View’ credentials link. This will display the API’s URL, your username, and password required to securely execute API calls. Save these somewhere safe as we will need them in the next step.

Testing The Personality Insights Service

To perform API calls, I am going to use Node.js. If you already have Node.js installed, you can move on to the next step; otherwise, follow the instructions to setup Node.js from the official download page.

To demonstrate how to use the Personality Insights, I am going to create a new Node.js project on my computer. With a command prompt open, navigate to the directory where your Node.js projects will be stored and create your new project:

mkdir watson-sentiments
cd watson-sentiments
npm init

To assist with making the API calls to Watson, I am going to leverage the NPM Package: Watson Developer Cloud Node.js SDK. This package can be installed via the command prompt:

npm install watson-developer-cloud --save

Before making the first call, the PersonalityInsightsV3 object needs to be instantiated with the credentials from the previous section. Begin by creating a new file called index.js that will contain the Node.js code.

Here is an example of configuring the class so it is ready to make API calls:

var PersonalityInsightsV3 = require(’watson-developer-cloud/personality-insights/v3’);
var personality_insights = new PersonalityInsightsV3(
  "url": "https://gateway.watsonplatform.net/personality-insights/api",
  "username": "**************************",
  "password": "*************",
  "version_date": "2017-12-01"
);

The personality_insights variable is what we will use to interact with the API for the Personality Insights service. Let’s review how to execute a call and return a personality profile:

var fs = require(’fs’);

personality_insights.profile(
"contentItems": [
   
         "content": "Some content that contains more than 100 words...",
         "contenttype": "text/plain",
         "created": 1447639154000,
         "id": "666073008692314113",
         "language": "en"
      
   ],
   "consumption_preferences": true
}, (err, response) => 
if (err) throw err;

fs.writeFile("results.txt", JSON.stringify(response, null, 2), function(err) 
if (err) throw err;

console.log("Results were saved!");
);
  });

The profile function accepts an array of contentItems. The ‘content’ item contains the actual content with a few additional properties identifying additional information to help Watson interpret it.

When this is executed, the results are written to a text file (the results are too large to write in the console). The result is an object that contains the following high-level properties:

  • word_count
  • The count of words interpreted
  • processed_language

The language that the content provided, e.g. (en).

  • Personality
    This is an array of the ‘Big Five’ personality characteristics (Openness, Conscientiousness, Extraversion, Agreeableness, and Emotional range). Each characteristic contains an overall percentile for that characteristic (e.g. 0.8100175318417588). To ascertain more detail, there is an array called children that provides more in-depth insight. For example, a child category under ‘Openness’ is ‘Adventurousness’ that contains its own percentile.
  • Needs
    This is an array of the twelve characteristics that define the aspects a person will resonate with a product (Excitement, Harmony, Curiosity, Ideal, Closeness, Self-expression, Liberty, Love, Practicality, Stability, Challenge, and Structure). Each characteristic contains a percentile of how the content was interpreted.
  • Values
    This is an array of the five characteristics that describe motivating factors that influence a person’s decision making (Self-transcendence / Helping others, Conservation / Tradition, Hedonism / Taking pleasure in life, Self-enhancement / Achieving success, and Open to change / Excitement). Each characteristic contains a percentile of how the content was interpreted.
  • Behavior
    This is an array that contains thirty-one elements. Each element provides a percentile of when the content was created. Seven of the elements define the days of the week (Sunday through Saturday). The remaining twenty-four elements define the hours of the day. This helps you understand when customer’s interact with your product.
  • consumption_preferences
    This is an array that contains eight different categories with as much as a twelve child categories providing a percentile of likelihood to pursue different products, services, and activities (Shopping, Music, Movies, Reading and learning, Health and activity, Volunteering, Environmental concern, and Entrepreneurship).
  • Warnings
    This is an array that provides messages if a problem was encountered interpreting the content provided.

Here is a CodePen of the formatted results:

See the Pen Example Watson Results by Jamie Munro (@endyourif) on CodePen.

Configuring Twitter

To search Twitter for relevant tweets, I am going to use the Twitter NPM package. From a console window where the application is hosted, run the following command to install:

npm install twitter --save

Before we can implement the Twitter package, you need to create a Twitter application.


Retrieving Twitter’s Access Tokens


Retrieving Twitter’s Access Tokens. (Large preview)

Once you’ve created your application, you need to retrieve the authorization keys required to perform API calls. With your application created, navigate to the ‘Keys’ and ‘Access Tokens’ page. Since we are not performing API calls against users of Twitter, OAuth integration is not required. Instead, we need only the four following keys:

  1. Consumer Key
  2. Consumer Secret
  3. Access Token
  4. Access Token Secret

The last two keys need to be generated near the bottom of the ‘Keys’ and ‘Access Tokens’ page. With the keys, here is an example of searching for Tweets about #SmashingMagazine:

var Twitter = require(’twitter’);

var client = new Twitter(
  consumer_key: ’*********************’,
  consumer_secret: ’******************’,
  access_token_key: ’******************’,
  access_token_secret: ’****************’
);

client.get(’search/tweets’,  q: ’#SmashingMagazine’ , function(error, tweets, response) 
if(error) throw error;

console.log(tweets);
);

The result of this code will log a list tweets about Smashing Magazine. For the purposes of this demonstration, the following fields are of interest to us:

  1. id
  2. created_at
  3. text
  4. metadata.iso_language_code

These are the fields we will feed Watson.

Integrating Personality Insights With Twitter

With Twitter setup and Watson setup, it’s time to integrate the two together and see the results. To make it interesting, let’s search for #DonaldTrump to see what the world thinks about the President of the United States. Here is the code example to search Twitter, feed the results into Watson, and write the results to a text file:

var fs = require(’fs’);
var Twitter = require(’twitter’);

var client = new Twitter(
  consumer_key: ’*********************’,
  consumer_secret: ’******************’,
  access_token_key: ’******************’,
  access_token_secret: ’****************’
);

var PersonalityInsightsV3 = require(’watson-developer-cloud/personality-insights/v3’);
var personality_insights = new PersonalityInsightsV3(
  "url": "https://gateway.watsonplatform.net/personality-insights/api",
  "username": "**************************",
  "password": "*************",
  "version_date": "2017-12-01"
);

client.get(’search/tweets’,  q: ’#DonaldTrump’ , function(error, tweets, response) 
if(error) throw error;

var contentItems = [];

// Loop through the tweets
for (var i = 0; i < tweets.statuses.length; i++) 
var tweet = tweets.statuses[i];

contentItems.push(
"content": tweet.text,
"contenttype": "text/plain",
"created": new Date(tweet.created_at).getTime(),
"id": tweet.id,
"language": tweet.metadata.iso_language_code
);
}

// Call Watson with the tweets
personality_insights.profile(
"contentItems": contentItems,
"consumption_preferences": true
, (err, response) => 
if (err) throw err;

// Write the results to a file
fs.writeFile("results.txt", JSON.stringify(response, null, 2), function(err) 
if (err) throw err;

console.log("Results were saved!");
);
});
});

Here is another CodePen of the formatted results that I received:

See the Pen Donald Trump Watson Results by Jamie Munro (@endyourif) on CodePen.

What Do The Results Say?

Once we’ve analyzed the ‘Openness’ trait of the ‘Big Five,’ we can infer the following:

  • Emotion is quite low at 13%
  • Imagination is average at 54%
  • Intellect is very high at 96%
  • Authority challenging is also quite high at 87%

The ‘Conscientiousness’ trait at a high-level is average at 46% compared with the ‘Openness’ high-level average of 88%. Whereas ‘Agreeableness’ is very low at only 25%. I guess people on Twitter don’t like to agree with Donald Trump.

Moving on to the ‘Needs.’ The sub-categories of ‘Curiosity’ and ‘Structure’ are in the 60 percentile compared to other categories being below the 10th percentile (Excitement, Harmony, etc.).

And finally, under ‘Values,’ the sub-category that stands out to me as interesting is the ‘Openness’ to ‘Change’ at an abysmal 6%.

Based on when you perform your search, your results may vary as the results are limited to the past seven days from executing the example.

From these results, I would determine that the average person who tweets about Donald Trump is quite intellectual, challenges authority, and is not open to change.

With these results, it would allow you to automatically alter how you would target your content towards your audience to match the results received. You will need to determine what categories are of interest and what percentiles do you wish to target. With this ammunition, you can begin automating.

What Else Can I Do With Watson?

As I mentioned at the beginning of this article, Watson offers many other different services. With these services, you could automate many different parts of common business processes. For example:

  • Building a chat bot that can intelligently answer questions based on a knowledge base of information;
  • Build an application where you dictate what you want written to Watson by using the speech to text functionality;
  • Automatically translate your content into different languages to create a multi-lingual site or knowledge base;
  • Teach Watson how to look for specific patterns in images. This could be used to determine if a logo is embedded into a photo.

This, of course, is a very small subset that my limited imagination can postulate. I’m sure you can think of many other ways to leverage Watson’s immense capabilities.

If you are looking for more examples, IBM has an entire GitHub repository dedicated to their Node.js SDK. The example folder contains over ten sample applications that convert speech to text, text to speech, tone analysis, and visual recognition to name just a few.

Conclusion

Before Watson can runaway with technological growth, resulting in the singularity where Artificial Intelligence destroys mankind, this article demonstrated how you can turn social media content into a powerful understanding of how the people creating the content think. Using the results from Watson, your application can use the categories of interest where the percentile exceeds or is less than a predetermined amount to change how you target your audience.

If you have other interesting uses of Watson or how you are using the Personality Insights, be sure to leave a comment below.

Smashing Editorial
(rb, ra, yk, il)


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Analyzing Your Company’s Social Media Presence With IBM Watson And Node.js

Ways To Avoid Overwhelming Users: Lessons Learned From My High-School Teachers

High school. I won’t lie: I did not have the highest grades in my graduating class. Some classes and lessons were so poorly designed and delivered that I would frequently become frustrated and fatigued and would ultimately shut down. The contents of the lessons would just wash over me. The experience wasn’t pleasant, and the results were obvious from my transcripts.
But I did well in a few classes. The major difference was the teaching style.

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Ways To Avoid Overwhelming Users: Lessons Learned From My High-School Teachers

Making The Web A Better Place: Guidelines For "Green" Web Design

Last month the first International Conference on Green Computing took place. The conference agenda included a broad range of topics but, in essence, was eagerly addressing issues surrounding the carbon footprint of computing and how computers can contribute to the well being of our world. So what better time to raise a few questions about green web design. What is our role in contributing to a greener computing world?

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Making The Web A Better Place: Guidelines For "Green" Web Design

Corporate Website Design: Creative and Beautiful Solutions

What do corporate websites have in common with other people’s children? Three things: they have their charm, like finger-paintings on the refrigerator; they can be useful, if infrequently; they are usually admired only by the people who created them. [Last updated: Dec/22/2016]
While designers know that a user’s experience on a website has a large impact on the way that customer will interact with them, impressing that concept on the corporate establishment has taken a very long time.

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Corporate Website Design: Creative and Beautiful Solutions