A jumble of letters of the English alphabet visualised lying in a chaotic pattern under blue skies.

Google Docs: A New Hope

I suspect the Google Docs grammar bot is the least useful bot there is. After hundreds of suggestions, I can think of only one instance in which it was right. Is its failure rate so high because it learns from how other people use English, instead of drawing from a basic ruleset?

I’m not saying my grammar is better than everyone else’s but if the bot is learning from how non-native users of the English language construct their sentences, I can see how it would make the suggestions it does, especially about the use of commas and singular/plural referents.

Then again, what I see as failure might be entirely invisible to someone not familiar with, or even interested in, punctuation pedantry. This is where Google Docs’s bot presents an interesting opportunity.

The rules of grammar and punctuation exist to assist the construction and inference of meaning, not to railroad them. However, this definition doesn’t say whether good grammar is simply what most people use and are familiar with or what is derived from a foundational set of rules and guidelines.

Thanks to colonialism, imperialism and industrialism, English has become the world’s official language, but thanks to their inherent political structures, English is also the language of the elite in postcolonial societies that exhibit significant economic inequality.

So those who wield English ‘properly’ – by deploying the rules of grammar and punctuation the way they’re ‘supposed’ to – are also those who have been able to afford a good education. Ergo, deferring to the fundamental ruleset is to flaunt one’s class privilege, and to expect others to do so could play out as a form of linguistic subjugation (think The New Yorker).

On the other hand, the problem with the populist ontology is that it encourages everyone to develop their own styles and patterns based on what they’ve read – after all, there is no one corpus of popular literature – that are very weakly guided by the same logic, if they’re guided by any logic at all. This could render individual pieces difficult to read (or edit).

Now, a question automatically arises: So what? What does each piece employing a different grammar and punctuation style matter as long as you understand what the author is saying? The answer, to me at least, depends on how the piece is going to find itself in the public domain and who is going to read it.

For example, I don’t think anyone would notice if I published such erratic pieces on my blog (although I don’t) – but people will if such pieces show up in a newspaper or a magazine, because newsrooms enforce certain grammatical styles for consistency. Such consistency ensures that:

  1. Insofar as grammar must assist inference, consistent patterns ensure a regular reader is familiar with the purpose the publication’s styleguide serves in the construction of sentences and paragraphs, which in turn renders the symbols more useful and invisible at the same time;
  2. The writers, while bringing to bear their own writing styles and voices, still use a ‘minimum common’ style unique to and associated with the publication (and which could ease decision-making for some writers); and
  3. The publication can reduce the amount of resources expended to train each new member of its copy-editing team

Indeed, I imagine grammatical consistency matters to any professional publication because of the implicit superiority of perfect evenness. But where it gets over the top and unbearable is when its purpose is forgotten, or when it is effected as a display of awareness of, or affiliation to, some elite colonial practice.

Now, while we can agree that the populist definition is less problematic on average, we must also be able to recognise that the use of a ‘minimum common’ remains a good idea if only to protect against the complete dilution of grammatical rules with time. For example, despite the frequency with which it is abused, the comma still serves at least one specific purpose: to demarcate clauses.

In this regard, the Google Docs bot could help streamline the chaos. According to the service’s support documentation, the bot learns its spelling instead of banking exclusively on a dictionary; it’s not hard to extrapolate this behaviour to grammar and syntactic rules as well.

Further, every time you reject the bot’s suggested change, the doc displays the following message: “Thanks for submitting feedback! The suggestion has been automatically ignored.” This isn’t sufficient evidence to conclude that the bot doesn’t learn. For one, the doc doesn’t display a similar message when a suggestion is accepted. For another, Google tracks the following parameters when you’re editing a doc:

customer-type, customer-id, customer-name, storageProvider, isOwner, editable, commentable, isAnonymousUser, offlineOptedIn, serviceWorkerControlled, zoomFactor, wasZoomed, docLocale, locale, docsErrorFatal, isIntegrated, companion-guest-Keep-status, companion-guest-Keep-buildLabel, companion-guest-Tasks-status, companion-guest-Tasks-buildLabel, companion-guest-Calendar-status, companion-guest-Calendar-buildLabel, companion-expanded, companion-overlaying-host-content, spellGrammar, spellGrammarDetails, spellGrammarGroup, spellGrammarFingerprint

Of them, spellGrammar is set to true and I assume spellGrammarFingerprint corresponds to a unique ID.

So assuming further that it learns through individual feedback, the bot must be assimilating a dataset in the background within whose rows and columns an ‘average modal pattern’ could be taking shape. As more and more users accept or reject its suggestions, the mode could become correspondingly more significant and form more of the basis for the bot’s future suggestions.

There are three problems, however.

First, if individual preferences have diverged to such an extent as to disfavour the formation of a single most significant modal style, the bot is unlikely to become useful in a reasonable amount of time or unless it combines user feedback with the preexisting rules of grammar and composition.

Second, Google could have designed each bot to personalise its suggestions according to each account-holder’s writing behaviour. This is quite possible because the more the bot is perceived to be helpful, the likelier its suggestions are to be accepted, and the likelier the user is to continue using Google Docs to compose their pieces.

However, I doubt the bot I encounter on my account is learning from my feedback alone, and it gives me… hope?

Third: if the bot learns only spelling but not grammar and punctuation use, it would be – as I first suspected – the least useful bot there is.

Injustice ex machina

There are some things I think about but struggle to articulate, especially in the heat of an argument with a friend. Cory Doctorow succinctly captures one such idea here:

Empiricism-washing is the top ideological dirty trick of technocrats everywhere: they assert that the data “doesn’t lie,” and thus all policy prescriptions based on data can be divorced from “politics” and relegated to the realm of “evidence.” This sleight of hand pretends that data can tell you what a society wants or needs — when really, data (and its analysis or manipulation) helps you to get what you want.

If you live in a country ruled by a nationalist government tending towards the ultra-nationalist, you’ve probably already encountered the first half of what Doctorow describes: the championship of data, and quantitative metrics in general, the conflation of objectivity with quantification, the overbearing focus on logic and mathematics to the point of eliding cultural and sociological influences.

Material evidence of the latter is somewhat more esoteric, yet more common in developing countries where the capitalist West’s influence vis-à-vis consumption and the (non-journalistic) media are distinctly more apparent, and which is impossible to unsee once you’ve seen it.

Notwithstanding the practically unavoidable consequences of consumerism and globalisation, the aspirations of the Indian middle and upper classes are propped up chiefly by American and European lifestyles. As a result, it becomes harder to tell the “what society needs” and the “get what you want” tendencies apart. Those developing new technologies to (among other things) enhance their profits arising from this conflation are obviously going to have a harder time seeing it and an even harder time solving for it.

Put differently, AI/ML systems – at least those in Doctorow’s conception, in the form of machines adept at “finding things that are similar to things the ML system can already model” – born in Silicon Valley have no reason to assume a history of imperialism and oppression, so the problems they are solving for are off-target by default.

But there is indeed a difference, and not infrequently the simplest way to uncover it is to check what the lower classes want. More broadly, what do the actors with the fewest degrees of freedom in your organisational system want, assuming all actors already want more freedom?

They – as much as others, and at the risk of treating them as a monolithic group – may not agree that roads need to be designed for public transportation (instead of cars), that the death penalty should be abolished or that fragmenting a forest is wrong but they are likely to determine how a public distribution system, a social security system or a neighbourhood policing system can work better.

What they want is often what society needs – and although this might predict the rise of populism, and even anti-intellectualism, it is nonetheless a sort of pragmatic final check when it has become entirely impossible to distinguish between the just and the desirable courses of action. I wish I didn’t have to hedge my position with the “often” but I remain unable with my limited imagination to design a suitable workaround.

Then again, I am also (self-myopically) alert to the temptation of technological solutionism, and acknowledge that discussions and negotiations are likely easier, even if messier, to govern with than ‘one principle to rule them all’.

Hey, is anybody watching Facebook?

The Boston Marathon bombings in April 2013 kicked off a flurry of social media activity that was equal parts well-meaning and counterproductive. Users on Facebook and Twitter shared reports, updates and photos of victims, spending little time on verifying them before sharing them with thousands of people.

Others on forums like Reddit and 4chan started to zero in on ‘suspects’ in photos of people seen with backpacks. Despite the amount of distress and disruption these activities, the social media broadly also served to channel grief and help, and became a notable part of the Boston Marathon bombings story.

In our daily lives, these platforms serve as news forums. With each person connected to hundreds of others, there is a strong magnification of information, especially once it crosses a threshold. They make it easier for everybody to be news-mongers (not journalists). Add this to the idea that using a social network can just as easily be a social performance, and you realize how the sharing of news can also be part of the performance.

Consider Facebook: Unlike Twitter, it enables users to share information in a variety of forms – status updates, questions, polls, videos, galleries, pages, groups, etc – allowing whatever news to retain its multifaceted attitude, and imposing no character limit on what you have to say about it.

Facebook v. Twitter

So you’d think people who want the best updates on breaking news would go to Facebook, and that’s where you might be wrong. ‘Might’ because, on the one hand, Twitter has a lower response time, keeps news very accessible, encourages a more non-personal social media performance, and has a high global reach. These reasons have also made Twitter a favorite among researchers who want to study how information behaves on a social network.

On the other hand, almost 30% of the American general population gets its news from Facebook, with Twitter and YouTube at par with a command of 10%, if a Pew Research Center technical report is to be believed. Other surveys have also shown that there are more people from India who are on Facebook than on Twitter. At this point, it’d just seem inconsiderate when you realize Facebook does have 1.28 billion monthly active users from around the world.

A screenshot of Facebook Graph Search.
A screenshot of Facebook Graph Search.

Since 2013, Facebook has made it easier for users to find news in its pages. In June that year, it introduced the #hashtagging facility to let users track news updates across various conversations. In September, it debuted Graph Search, making it easier for people to locate topics they wanted to know more about. Even though the platform’s allowance for privacy settings stunts the kind of free propagation of information that’s possible on Twitter (and only 28% of Facebook users made any of their content publicly available), Facebook’s volume of updates enables its fraction of public updates rise to levels comparable with those of Twitter.

Ponnurangam Kumaraguru and Prateek Dewan, from the Indraprastha Institute of Information Technology, New Delhi (IIIT-D), leveraged this to investigate how Facebook and Twitter compared when sharing information on real-world events. Kumaraguru explained his motivation: “Facebook is so famous, especially in India. It’s much bigger in terms of the number of users. Also, having seen so many studies on Twitter, we were curious to know if the same outcomes as from work done on Twitter would hold for Facebook.”

The duo used the social networks’ respective APIs to query for keywords related to 16 events that occurred during 2013. They explain, “Eight out of the 16 events we selected had more than 100,000 posts on both Facebook and Twitter; six of these eight events saw over 1 million tweets.” Their pre-print paper was submitted to arXiv on May 19.

An upper hand

In all, they found that an unprecedented event appeared on Facebook just after 11 minutes while on Twitter, according to a 2014 study from the Association for the Advancement of Artificial Intelligence (AAAI), it took over ten times as longer. Specifically, after the Boston Marathon bombings, “the first [relevant] Facebook post occurred just 1 minute 13 seconds after the first blast, which was 2 minutes 44 seconds before the first tweet”.

However, this order-of-magnitude difference could be restricted to Kumaraguru’s choice of events because the AAAI study claims breaking news was broken fastest during 29 major events on Twitter, although it considered only updates on trending topics (and the first update on Twitter, according to them, appeared after two hours).

The data-mining technique could also have played a role in offsetting the time taken for an event to be detected because it requires the keywords being searched to be manually keyed. Finally, the Facebook API is known to be more rigorous than Twitter’s, whose ability to return older tweets is restricted. On the downside, the output from the Facebook API is restricted by users’ privacy settings.

Nevertheless, Kumaraguru’s conclusions paint a picture of Facebook being just as resourceful as Twitter when tracking real-world events – especially in India – leaving news discoverability to take the blame. Three of the 16 chosen events were completely local to India, and they were all accompanied by more activity on Facebook than on Twitter.


Even after the duo corrected for URLs shared on both social networks simultaneously (through clients like Buffer and HootSuite) – 0.6% of the total – Facebook had the upper hand not just in primacy but also origin. According to Kumaraguru and Dewan, “2.5% of all URLs shared on Twitter belonged to the facebook.com domain, but only 0.8% of all URLs shared on Facebook belonged to the twitter.com domain.”

Facebook also seemed qualitatively better because spam was present in only five events. On Twitter, spam was found to be present in 13. This disparity can be factored in by programs built to filter spam from social media timelines in real-time, the sort of service that journalists will find very useful.

Kumaraguru and Dewan resorted to picking out spam based on differences in sentence styles. This way, they were able to avoid missing spam that was stylistically conventional but irrelevant in terms of content, too. A machine wouldn’t have been able to do this just as well and in real-time unless it was taught – in much the same way you teach your Google Mail inbox to automatically sort email.

Digital information forensics

A screenshot of TweetCred at work. Image: Screenshot of TweetCred Chrome Extension
A screenshot of TweetCred at work. Image: Screenshot of TweetCred Chrome Extension

Patrick Meier, a self-proclaimed – but reasonably so – pioneer in the emerging field of humanitarian technologies, wrote a blog post on April 28 describing a browser extension called TweetCred which is just this sort of learning machine. Install it and open Twitter in your browser. Above each tweet, you will now see a credibility rating bar that grades each tweet out of 7 points, with 7 describing the most credibility.

If you agree with each rating, you can bolster with a thumbs-up that appears on hover. If you disagree, you can give the shown TweetCred rating a thumbs down and mark what you think is correct. Meier makes it clear that, in its first avatar, the app is geared toward rating disaster/crisis tweets. A paper describing the app was submitted to arXiv on May 21, co-authored by Kumaraguru, Meier, Aditi Gupta (IIIT-D) and Carlos Castillo (Qatar Computing Research Institute).

Between the two papers, a common theme is the origin and development of situational awareness. We stick to Twitter for our breaking news because it’s conceptually similar to Facebook, fast and importantly cuts to the chase, so to speak. Parallely, we’re also aware that Facebook is similarly equipped to reconstruct details because of its multimedia options and timeline. Even if Facebook and Twitter the organizations believe that they are designed to accomplish different things, the distinction blurs in the event of a real-world crisis.

“Both these networks spread situational awareness, and both do it fairly quickly, as we found in our analysis,” Kumaraguru said. “We’d like to like to explore the credibility of content on Facebook next.” But as far as establishing a mechanism to study the impact of Facebook and Twitter on the flow of information is concerned, the authors have exposed a facet of Facebook that Facebook, Inc., could help leverage.