Presidential speech word patterns

In the continuing saga of presidential campaign speech language, I’ve been analyzing parts of speech that don’t get much attention such as verbs, adverbs, and adjectives. Looking at the way in which each candidate uses such words over time turns up some interesting patterns. I don’t understand their deep significance, but there’s some work suggesting that variability in writing is a sign of health; and Ashby’s Law of requisite variety can be interpreted to mean that the actor in a system with the most available options tends to control the system.

Here are the plots of adjective use (in a common framework) for the 2008 and 2012 candidates (up to the time that Santorum dropped out of the race).

It’s striking how much the patterns over time form a kind of spiral, moving from one particular combination of adjectives to another and another and eventually back to the original pattern. The exception is Obama who displays a much more radial structure, with an adjective combination that he uses a lot, and occasional deviations to something else, but a rapid return to his “home ground”.

You can see (the extremal set of) adjectives and their relationships in this figure:

You can see that they form 3 poles: on the left, adjectives associated with energy policy; at the bottom, adjectives associated with patriotism; and on the right, adjectives associated with defence [yes, it is spelled that way]. This figure can be overlaid on those of the candidates to get a sense of which poles they are visiting. For example, Obama’s “home ground” is largely associated with the energy-related adjectives.

Comparing content in the US presidential campaign 2008 vs 2012

I posted about the content in the 2012 presidential campaign speeches. It’s still relatively early in the campaign so comparisons aren’t necessarily going to reveal a lot, but I went back and looked at the speeches in 2008 by Hillary Clinton, McCain, and Obama; and compared them to the four remaining Republican contenders and President Obama so far this year.

Here’s the result of looking just at the nouns:

The key is:   Clinton — magenta circles; Obama 2008 — red circles, McCain — light blue stars;

Gingrich — green circles; Paul — yellow circles; Romney — blue circles; Santorum — black circles; Obama 2012 — red squares.

Recall that the way to interpret these plots is that points far from the origin are more interesting speeches (in the sense that they use more variable word patterns) while different directions represent different “themes” in the words used.

The most obvious difference is that the topics talked about were much more wide-ranging in 2008 than they have been this year. This may be partly because of the early stage of the campaign, the long Republican primary season keeping those candidates focused on a narrow range of topics aimed at the base, or a change in the world that has focused our collective attention on different, and fewer, topics.

This can be teased out a bit by looking at the words that are associated with each direction and distance. The next figure shows the nouns that were actually used (only those that are substantially above the median level of interestingness are labelled):

You can see that there are four “poles” or topics that differentiate the speech content. To the right are words associated with the economy, but from a consumer perspective. At the bottom are words associated with energy. To the left are actually two groups of words, although they interleave a little. At the lower end are words associated with terrorism and the associated wars and threats. At the upper end are words associated with the human side of war and patriotism.

These two figures can be lined up with each other to get a sense of which candidates are talking about which topics. The 2012 speeches and Obama’s 2008 speeches all lean heavily towards the economic words. In 2008, McCain and Clinton largely talked about the war/security issues, with a slight bias by Clinton towards the patriotism cluster.

Obama’s 2012 speeches tend towards the energy cluster but, at this point, quite weakly given the overall constellation of topics and candidates.

The other thing that is noticeable is how similar the topics for some of the Republican contenders are: their speeches cluster quite tightly.

Negative words in the campaign

Yesterday we looked at the use of positive words in the campaign. Today, I want to present the use of negative words.

We saw the President Obama is much better at using positive words than the Republican contenders; but they are all about the same at using negative words. Note that these two flavors of words are not necessarily opposites; someone can use both positive and negative words at high rates (although that itself might be interesting).

Here are the speeches according to their patterns of negative word use:

Again, distance from the origin indicates intensity of negative word use, and direction indicates different words being used.

Romney has the strongest use of negative words (and the associated words are ones like “disappointments” and “worrying”). Ron Paul also has quite strong use of negative words. His word choices are quite different from those of the other candidates, though; they include “bankrupt”, “flawed” and “inconvenient”.

President Obama and Gingrich have moderate levels of negative word use; the most popular word for both of them is “problem”, followed by “challenge”.

Santorum has the lowest levels of negative word use of all five of them.

The differences are interesting because they shed some light on how each candidate views those aspects of the situation that are not favorable to them. Obama and Gingrich have a more proactive view: negatives to them are problems. The other candidates have a more outward focus on the source of difficulties and, at the same time, a more negative inward focus, that is they use negative words that reflect how they feel about themselves.

I also ran an experiment weighting the positive words positively and the negative words negatively, to see if there is any ranking from, as it were, most positive person to most negative person. It turns out that there isn’t such a ranking. All of them use mixtures of positive and negative words, different mixtures for each, but all of about the same ratio of positivity to negativity.

Positive words in the campaign

Yesterday I posted about the content of the speeches of the campaigners for the 2012 presidential election cycle: the Republican contenders and President Obama. Today I have similar results for the use of positive words.

Here are the speeches:

The figure should be interpreted like this:  distance from the origin indicates intensity of positive word use; direction indicates the use of a different set of positive words. So President Obama is much more positive than the Republican contenders, of which Gingrich is noticeably more positive than the rest. These are only based on the use of positive words so a placement close to the origin should be interpreted as the absence of positive words, not any kind of negativity (stay tuned). In other words, speeches near the origin are not positive (they could be either neutral or negative but this analysis can’t differentiate).

Some of the positive words associated with President Obama are: “profitable”, “creative”, “efficiency” and “outstanding”.

Some of the positive words associated with Gingrich are: “tremendous”, “optimistic”, “gains”, “happiness”, and “positive” itself.

You can see why the Republican approval numbers are dropping — people pick up on the tone of speeches, and they are attracted to positive language — which they aren’t getting. Even Gingrich’s positive words are mostly about the improvement (perceived) in his chances, not in the wider US situation.

Content in Presidential Campaign Speeches

Last week I posted details of the level of “persona deception” among the Republican presidential candidates and President Obama. Persona deception measures how much a candidate is trying to present himself as “better” in some way than he really is. This is the essence of campaigning — we don’t elect politicians based on the quality of their proposals; and we don’t fail to elect them because they tell us factual lies. Almost everything is based on our assessment of character which we get from appearance and behavior, and also from language.

Today I’ll post a description of the different content of the speeches so far in 2012. This is less informative than levels of deception, but it does give some insight into what candidates are thinking is of interest or importance to the voters they are currently targeting. Here is an overview of the topic space:

You can see that most of the Republican candidates are talking about very similar things. In fact, the speeches in the upper right-hand corner are associated strongly with words such as “greatness”, “freedom”, “opportunity”, “principles” and “prosperity” — all very abstract nouns without much content that could come back to haunt them.

Gingrich’s speeches towards the bottom of the figure are quite different, although still associated with quite abstract words: “bureaucracy”, “media”, “pipeline”, “elite”, “establishment”. These are almost all things that he is against — stay tuned for an analysis of negative word use later in the week.

Obama’s speeches, on the left-hand side, are heavily oriented to manufacturing associated with words such as: “cars”, “hi-tech”, “plant”, “oil”, “demand”, “prices”.

What a candidate chooses to talk about seems to be a mix of his personal hobbyhorses (at the time) and some judgement of what issues are of interest to the general public, or at least which can create daylight between one candidate’s position and the others. From this perspective, Gingrich separates himself from the other Republicans quite well. Somewhat surprisingly, Ron Paul’s content is not very different from that of Romney and Santorum. Probably this can be accounted for as a function of the three of them all trying to appeal to a very similar segment of the base. Whether Gingrich is consciously trying to address different issues, or whether his history or personality compel him to is not clear.

2012 US Election, Republicans plus President Obama

Yesterday I posted details about the levels of persona deception in the speeches by the Republican candidates since the beginning of 2012. In striking contrast to the 2008 cycle, the speeches fall along a single axis, indicating widespread commonalities in the way that they use words, particularly the words of the deception model.

Today I’ve included President Obama’s speeches this year in the mix. I’ve tried to select only those speeches where there was an audience. Of course, for a sitting president, the distinction between an ordinary speech and a campaign speech is difficult to draw. Almost all of these are labelled as campaign events at whitehouse.gov.

Here is the plots of the persona deception levels, with Obama’s speeches added in magenta.

Generally speaking, Obama’s levels of persona deception (see yesterday’s post to be clear on what this means) are in the low range compared to the Republican presidential candidates. This is quite different from what happened in the 2008 cycle, where his levels were almost always well above those of McCain and Clinton. It’s not altogether surprising, though. First, he can no longer be the mirror in which voters see what they want to see since he has a substantial and visible track record. Second, he doesn’t have to try as hard to project a persona (at least at this stage of the campaign) since he has no competitor. I expect that his values will climb as the campaign progresses, particularly after the Republican nominee becomes an actual person and not a potential one.

The interesting point is the outlier at the top left of the figure. This is Obama’s speech to AIPAC. Clearly this is not really a campaign speech, so the language might be expected to be different. On the other hand, if it were projected onto the single-factor line formed by the other speeches, it would be much more towards the deceptive end of that axis. Since the underlying model detects all kinds of deception, not just that associated with persona deception in campaigns, this may be revealing of the attitude of the administration to the content expressed in this speech.

Republican presidential candidates — first analysis of persona deception

Regular readers of this blog will know that I carried out extensive analysis of the speeches of the contenders in the 2008 US presidential election cycle (see earlier postings). I’m now beginning similar analysis for the 2012 cycle, concentrating on the Republican contenders for now.

You will recall that Pennebaker’s deception model enables a set of documents to be ranked in order of their deceptiveness, detected via changes in the frequency of occurrence of 86 words in four categories: first-person singular pronouns, exclusive words, negative-emotion words, and action verbs. Words in the first two categories decrease in the presence of deception, while those in the last two categories increase. The model only allows for ranking, rather than true/false determination, because “increase” and “decrease” are always relative to some norm for the set of documents being considered.

How does this apply to politics? First of all, the point isn’t to detect when a politician is lying (Cynical joke: Q: How do you tell when a politician is lying? A: His lips are moving). Politicians tell factual lies, but this seems to have no impact on how voters perceive them, perhaps because we’re come to expect it. Rather, the kind of deception that is interesting is the kind where a politician is trying to present him/herself as a much better person (smarter, wiser, more competent) than they really are. This is what politicians do all the time.

Why should we care? There are two reasons. The first is that it works — typically the politician who is able to deliver the highest level of what we call “persona deception” gets elected. Voters have to decide on the basis of something, and this kind of presentation as a great individual seems to play more of a role than, say, actual plans for action.

Second, though, watching the changes in the levels of persona deception gives us a window into how each candidate (and campaign) is perceiving themselves (and, it turns out, their rivals) from day to day. Constructing and maintaining an artificial persona is difficult and expensive. Levels of persona deception tend to drop sharply when a candidate becomes confident that they’re doing well; and when some issue surfaces about which they don’t really have a persona opinion because, apparently, it takes time to construct the new piece.

So, with that preliminary, on to some results.

The figure shows the speeches in a space where speeches with greater person deception (spin) are further to the right, and those with less persona deception are further to the left. Ron Paul shows the lowest level of persona deception which is not surprising — nobody has ever accused him of trying to be what he is not. In contrast, Romney shows the highest level of persona deception — again not surprising as he has had to try hardest to make himself appealing to voters. Note that this also predicts that he will do well. Both Gingrich and Santorum occupy the middle ground; both are running on a very overt track record and are not trying as hard to make themselves seem different from who they are. Indeed, candidates with a strong history tend to have lower levels of persona deception simply because it’s very difficult to construct a new, more attractive persona when you already have a strong one. (The two points vertically separated from the rest are the result of a sudden burst of using “I’d” in these two speeches.)

The following figures break out the temporal patterns for the four candidates:

What’s striking about Romney is how much the level of persona deception changes from speech to speech. In the last election cycle, this wasn’t associated with audience type or recent success but seemed to be much more internally driven. This zig-zag pattern is much more the norm than a constant level of persona deception — some mystery remains.

Language in Presidential Elections — 2012 Season Opener

Readers of this blog will know that we spent a lot of time analyzing the speeches of the U.S. presidential candidates in the 2008 election. Our primary interest was in the use of the deception model, a linguistic/textual model of how freeform language changes when the speaker/writer is being deceptive.

In the political arena, factual deception, saying things that just ain’t so, plays very little role, perhaps because voters have very low expectations of politicians in this area. What we call persona deception, presenting oneself as a better,wiser,  more powerful, more able, more knowledgeable person than one really is is the heart of successful campaigning. It turns out that the deception model captures deception across the whole range from factual to persona deception, so it gives us a lens to look at candidates and campaigns. What’s more, because language generation is almost entirely subconscious, this lens is hard to fool.

The most important skill candidates and their campaigns have is the ability to reach out to potential voters to convince them that they are better than the other possibilities. The language that they use is an important channel, especially in settings where everyone is conservatively dressed, and standing behind a podium that conceals most of their body language, as the Republican presidential field was in Iowa yesterday.

Strong candidates understand, at least instinctively, that they are not making arguments to convince voters, but presenting themselves as more compelling human beings. Our analysis of the speeches of candidates in the 2008 U.S. presidential election showed that candidates use three different kinds of speeches: blue skies speeches that promise generically good things and could be delivered interchangeably by any candidate – they are aimed at a wide audience; track record speeches that use past achievements to imply special qualifications for future achievements – they are aimed at swing voters; and manifesto speeches that describe a candidate’s personal qualities directly – they are aimed at a candidate’s base and reinforce common identity. But in all three cases, it’s not the content of the speech that matters, but what it implies about the speaker.

Our analysis in the last election cycle showed that Obama was by far the best as presenting himself as a wonderful person, and many voters, and certainly many in the media, projected onto the persona  positive qualities that were perhaps not there. Interestingly, yesterday was the first time I have seen open Democratic buyers remorse about electing Obama, something I predicted would happen from the analysis we did.

The Republican candidates’ debate in Ames showed what a shaky grasp many of the candidates have on how to be a convincing candidate. Of course, this venue was a difficult one. Its overt purpose was for candidates to explain themselves to the local Republican base ahead of the Ames Straw Poll,which would have required largely manifesto content; but national television coverage made it an unmissable opportunity to reach out to a wider, but much more diverse audience, suggesting track record content. Blue skies content is always dangerous in the early stages of a campaign because grand but potentially unwise statements can come back to haunt a candidate.

Manifesto content was indeed popular – for example, we learned how many children almost every candidate has – typical content aimed at the base (“I’m a parent just like you”). Several candidates also tried for track record content, but got it quite wrong. The purpose of a track record speech is not for candidates to read their resumes to the audience; it’s to make the argument “I was able to do A, so you can trust me to be able to do similar-but-larger B” and this second part was notably absent.

Voters also want candidates to be sincere — recall the famous quotation “The secret of success is sincerity. Once you can fake that you’ve got it made” (Jean Girardoux). This is not just a cute quotation; this is what good politicians are able to do. In Iowa, this was another area where almost everyone stumbled. It was clear that most of the candidates had not only prepared talking point responses to probable questions, but has also rehearsed actual answers. Delivering from a prepared and memorized script and seeming sincere is a difficult business, and actors who can do it reliably command high rewards.  Most of the candidates failed at seeming sincere. Several managed the worst of both worlds by trying to combine their prepared scripts with some ad libbing and came across as quite incoherent. One of the reasons for Gingrich’s strong showing is that he stayed away from scripts and delivered his answers as if he had just thought of them. Huntsman and Romney, in contrast, were especially wooden.

When humans listen to humans, the content matters. But when character is the issue, other aspects of language matter more. Much language generation is subconscious, and therefore beyond a candidate’s control. This is good for voters because it means we can sometimes see through to the real person no matter how sophisticated their speech writers and spin doctors.

Metaphors and counterterrorism

The Intelligence Advanced Research ProjectsActivity (IARPA) has a call out for proposals to develop a system that will extract metaphors from text. The assumption is that the metaphors that are used in a document, or a community, reflect a way of viewing and organizing the world that can provide a higher-level way to understand other (sub)cultures. This seems like a very difficult challenge, which is exactly what these funding agencies derived from DARPA are supposed to do.

I remember reading a paper that Charles Williams presented to the Inklings (the Oxford society that included C.S. Lewis, Tolkien, and other high fliers) in which he talked about just how difficult it is to understand what a metaphor does (I haven’t been able to find either paper or reference). Similes are (by comparison) straightforward; when we say “A is like B” we draw attention to or highlight some aspect of B that is similar to that of A, and therefore emphasize some aspect of A, perhaps one that isn’t obvious.

A metaphor is a much more difficult object. When we say “A is B” we could take the view that this is just a more obscure kind of simile, in which the reader/hearer is invited to conceive of the possible similarity without a hint from the writer/speaker. But Williams argues, and I agree, that more is going on here. For a start, metaphors are not symmetric: if I say “A is B” it’s often nonsense to say “B is A” whereas similes usually are symmetric. Often there is no obvious and straightforward way to reduce a metaphor to a simile, that is there is no small set of properties common to A and B. And yet metaphors can be powerful.

There is a little relevant work in psychology, most of it associated with Judy DeLoache and what’s called the Dual Representation Hypothesis. Roughly speaking, the idea is that brains are well-equipped to represent symbols and the things they denote and to map computations on the symbols to computations on the denoted things in usable ways (apologies to psychologists for this mangled and computational perspective).  This goes some way to explain abstract reasoning, with some very nice experiments with young children showing when various levels of sophistication kick in; but it might also provide some explanatory power for metaphors. Unfortunately, there is some evidence that the more black-box the symbol, the more usable it is, which is evidence against this being a useful explanation for metaphors.

I won’t be applying for funding to work on this — but I’ll be watching the results with interest.

And Williams’ conclusion — that metaphors are something like a legal fiction; which I didn’t find very convincing at the time I read the article and still don’t.

Deception scores for the UEA emails

I’ve also calculated the deception scores for the UEA “climategate” emails, using the same methodology that I’ve written about in the context of the speeches of presidential candidates.

This doesn’t (yet) give any great results. This is partly because deception scores can only be computed for sets of similar documents. The UEA emails, however, fall into two broad classes: simple emails, and discussions and suggestions about more formal documents (papers and grant proposals). The language in these two classes is quite different, which makes them difficult to compare. For example, the base rates of first-person singular pronouns are very different.

What I have done is to see whether there are any patterns in  deception scores with time. A strong change in either class of email should be detectable as a variation of score with time, which might be visible. The result is shown below, with the deception score axis running from right (low) to left (high), and the markers getting lighter with the passage of time.

Deception scores of UEA emails

The only thing that strikes me so far is that many emails with low deception scores are older in time. This might be taken to indicate some kind of change in the language patterns of these email users.

The released emails are a small and not very random set of all of the emails sent by these individuals. So not too much should be read into this plot.

Patterns of word usage in the UEA climate emails

I’m always pleased to see examples of real emails because they can act as testbeds for various textual analysis techniques. I’ve begun to analyse the “climategate” emails from the University of East Anglia. The figure below shows a plot of the structure of the words used. (This is quite a quick and dirty analysis — I didn’t try to remove email headers or otherwise clean up the content of the files.)

There are three parts to the structure. The arm to the right is an artifact of the fact that several word files were included in the bodies of emails, rather than as attachments, so my extraction software sees them as part of the text. This can be fixed, but will take me some time.

The interesting property is the longtitudinal structure from top to bottom in the figure. The phrases at the bottom are all content, while the phrases at the top are all identifiers of people and places (admittedly hard to see). Since the analysis algorithms know nothing of the semantics of emails, and are based purely on “bag of words” style analysis, this is an interesting, and unexpected, outcome.

Content in election speeches

I went back and looked at the collection of speeches by the three contenders in the 2008 U.S.presidential election, not from the point of view of spin, but just looking at what they were about. The results were a little surprising. Despite all of the thinking and polling that goes into a campaign, the speeches were about only three topics.

Structure of word usage in speeches

These three topics are: the economy, associated with words such as crisis, jobs, plan, pay; security, associated with words such as war, threat, Iraq, allies; and family, associated with words such as parents, children, living. This last group features only early in the campaign and a single speech by Obama given on Fathers Day.

It’s no surprise that these topics appear. What does seem surprising is how little else was mentioned: other topics words are all hidden in the center of the figure indicating that their pattern of appearance in not very interesting.

What can be learned from text III

Another property that can be learned from text is the author’s attitude to whatever the text is about. This is called, variously, sentiment analysis or appraisal theory. For obvious reasons, it has always been interesting to advertisers and marketers.

In its simplest form, it just analyzes text for associations of adjectives with the nouns of interest, for example films or people. This could be as simple as seeing whether the adjective “good” or “bad” appears near the noun(s) in question. It is not too difficult to extend this to other sets of adjectives that can be considered positive or negative: “the movie was exciting” (good), or “the movie was boring” (bad).

However, this process is not quite as easy as it looks. First of all, it’s hard in languages like English to be sure which adjective goes with which noun — proximity in the sentence is often used, but this is not very robust: “Although parts of the movie were good, overall it was bad” is not a positive comment about the movie.

Second, authors often use devices such as irony and sarcasm which look, syntactically, as if they are giving one opinion, but are actually giving the opposite opinion. Humans figure this out using deep background knowledge about the situation and about human mental life, so it’s difficult for an algorithm to mimic this level of understanding.

Third, texts often comment about the parts of an object as well as the whole object, and it becomes difficult to decide which adjectives go with which parts.

There are three levels of algorithmic analysis used for this problem:

1. Using simple sets of opinion adjectives (and maybe other words) and trying to associate them to the nouns of interest using proximity, perhaps with a little extra sophistication, trying to pick out dependent clauses etc.
2. Parsing the text more deeply and using natural language analysis techniques to associate opinion words with the nouns of interest.
3. Using systemic functional linguistics approaches, which treat language generation as a goal-driven task by an individual in a societal setting, as well as a technology.

These levels are arranged in increasing order of sophistication, and also of complexity. However, even the best algorithms perform only at the 80% or so level, and that’s only capturing relatively unsophisticated judgements.

There are obvious applications to sentiment analysis in adversarial situations: trying to decide whether a terrorist group pronouncement or a threat represents a genuine opinion by the author or some form of propaganda; and who the propaganda might be aimed at.

What can be learned from text II

Today let me talk about the internal state channel and what can be learned from it.

First, let me point out that this channel is even more driven by subconscious processes, so we have very little control over it, even if we know how it works. This makes it very revealing.

Some of the properties that can be inferred from the internal state channel are:

• personality. This is useful in many adversarial situations because you can’t usually get an adversary to sit down and take an MMPI test. Several ways to categorize personality from word usage have been developed, although they are obviously somewhat limited.
• status of each participant in a conversation. In general, lower status participants tend to use first-person singular pronouns at higher rates, which gives clues about how each participant regards him/herself with respect to the others involved.
• health. The health of an individual going forward for several months can be predicted by flexibility in pronoun use. This is a bit different from the other categories because it doesn’t rely on a particular signature of word frequencies, but on the ability of each individual to vary his/her word usage widely over time. In other words, unhealthy people maintain a single perspective on the world, and so use consistent pronouns to describe themselves and those around them; healthier people have a changing perspective that is reflected in changing pronouns. (Note the connections to first position, second position etc. associated with NLP and its antecedent psychological approaches.)
• stress/depression. This is related to the previous category, but both stress and depression show up in characteristic ways in word usage. In the case of depression, the changes continue even after the depression ends, so that the never-depressed differ from the once-depressed.
• community involvement or embeddedness. The way in which first-person singular and plural pronouns are used gives clues about how an individual feels in relation to a community.
• deception. I’ve written extensively about this in previous posts.

Much of these results are the work of James Pennebaker and his group at the University of Texas at Austin. His work (here) is quite accessible. The paper by Chung and Pennebaker is particularly relevant.

What can be learned from text?

I’ve blogged before about text as a way of seeing both a content channel and an internal state channel from its author’s mind; and I’ve talked about looking for deception in the internal state channel extensively in the context of politics.

What else can be learned from text?

From the content channel, these kind of things can be extracted:

• the names of people, places, things, and sometimes other specifics such as money (these are called named entities).
• the topic of a text, that is what it is about expressed as a single word or phrase (or perhaps a short list of such things). This property is what is used to collect together similar stories in Google News, for example.
• events that occurred in the text. Although this seems straightforward, it is actually quite difficult and little work has been done on it.
• a summary of the text. There is a wide range of possible summary styles: a tag cloud is one kind of summary; the snippet that comes below the url in a page of seach results is another; but there are also summaries that consist of sets of sentences drawn from the text that represent the main ideas or content.
• a narrative from the text. Summaries try to cherry pick ideas from content, while a narrative tries to provide some sense of the flow of events.

All of these techniques make it easier to take a text, usually one of a very large number of texts, and decide whether it is worth looking at the raw text or not.

Other properties of the content channel that are often of interest are:

• who the author is. This is an old kind of question, going back to questions like: Are Shakespeare’s plays written by Shakespeare, or the Earl of Essex, or someone else. It’s actually a range of questions: was this text written by author A or someone (anyone) else; was this text written by author A or author B; or match this large set of documents and this large set of authors.
• properties of the author. For example, there is some work on determining author gender, author age, and author first language.

All of these problems have been studied, and many can be solved with knowledge discovery techniques whose accuracies are in the range 70-85%. This sounds pretty good, but is not terribly practical when the set of texts involved is in the tens of thousands. Most people who use Google News will have come across the occasional howler topic that is completely useless, for example.

Text in Adversarial Situations

Whenever we, as humans, write or speak we reveal something about ourselves. Part of this is what we want to reveal — the purpose of our communication. But we also reveal a great deal that we did not necessarily intend to reveal, and this is part of what makes textual analysis interesting in adversarial situations.

It’s helpful to think of what is happening when we speak or write as happening simultaneously in two channels:

1. The content channel, which serves the purpose for which the communication is intended, and is often carried by the `big’ words: nouns and verbs; and
2. The internal state channel, which reveals information about our mental state, intentions, and feelings, and is often carried by the `little’ words such as conjunctions and verbs.

The internal state channel is what we examined when looking for deception in earlier posts.

We tend to think, intuitively, that we control the content channel consciously, although we can’t control the internal state channel. In fact, we actually do not control either channel very well, at the level of details; although, of course, when we set out to say something we usually manage to get the content we want across.

When we think about communication emanating from bad guys, there are a number of different scenarios:

1. They are communicating in a public way to disseminate content. They may attempt concealment, but are aware that their communication could be intercepted both accidentally by almost anyone, and by people looking for it explicitly.
2. They are communicating in a private way to disseminate content. They will have to attempt concealment, and know that communication that is intercepted will be scrutinized carefully.
3. They are communicating in a public way, but it is their mental state that is most interesting. For example, propaganda is a form of content-filled public communication, but the mental models and intentions behind it are probably of more interest than the content.

Each of these scenarios requires a particular kind of analysis, but the overall structure is the same:

• Use selection to find potentially interesting communication in the mass of communication in today’s media and internet. This relies on modelling normality; looking for concealment; and looking for evasion (reaction to simple selection techniques).
• Use analysis techniques on the set of selected communications to extract content, authorship information, metainformation (e.g. traffic analysis), intention, emotional state, deception, and attitudes.

I’ll talk in more detail about these aspects in subsequent posts.