Category Archives: Interactive

Force-Directed Diagram: Memcons and Telcons ‘Textplot’


Static ‘Textplot’ of both corpora

This is a network of the 1300 most frequent words in each corpus, related according to their mutual similarity in probability distributions across the span 1969-1977. This was accomplished using the fabulous ‘textplot‘ software, written by David McClure.

In both cases, the general time axis is left-to-right (the layouts were rotated in gephi after the gml files were generated, and then those gephi-generated files were run through the ‘kissinger’ branch of the software found at the humanist github repository.

In the memcons, the ‘tendrils of specificity’ (the long patterns of increasingly specific words emerging like pseudopods in similarly-colored Modularity Classes from each diagram’s center) relate quite distinctly to areas of geopolitical focus, such as the Soviet Union, Japan, China, the Middle East, and Vietnam, among others.

Memcons ‘Textplot’KT3-stop

In the Telcons textplot, the ‘tendrils’ are most closely related to what appear to be large swaths of the telcons bearing the varying stamps marking the documents’ former classification and declassification statuses:


There are also some clusters that appear to be based around geopolitical topics (those related to Vietnam, for example). Also noteworthy are: 1., a grouping that appears related to a section of the documentation with increased OCR error rates or other improperly converted material (this grouping may also reflect the use of initials in the transcripts, although it’s unclear at this time to what degree.) and 2., the placement of the first names of Kissinger’s wife Nancy and son David, distinctly outside the general word networks.

Telcons ‘Textplot’KA3-stop

It’s important to note that while there are certainly similarities between the nodes comprising the various ‘tendrils of specificity’, textplot’s similarity calculation is based on a calculation of word frequency across the corpus as a whole, without distinction at the document level. This can result in contrasting results to collocation, topic modeling and other analyses that can operate at the document or ‘chunk’ level, and the difference can be instructive in some cases.

For example, the presence of ‘bombing’ is among the most frequent 1300 words in the telcons, nestled tightly within the cluster of words related to Vietnam. The word does not appear among the most frequent in the memcons. Given the differences in word composition between the two corpora (familiarity, % of nouns/place names, formailty, detail, provenance, redaction, etc.), this is to be expected, but nevertheless the word’s presence is still interesting for a few reasons.

Zoomed-in on ‘bombing’ within the
Vietnam-related cluster of telcons document Bombing-KATextplotCloseup

Among a number of possible reasons, the finding is interesting because coming as a recent finding (2014-2015) it is a non-linear ‘post-indication’ of the value of the earlier decision (2012-2013) to do collocation analysis using bombing as the target word. This is especially true given the resulting finding that indicates a potentially significant distribution in the collocation MI-scores between ‘bombing’ and those words describing Vietnam, versus those that describe the country’s neighbors in Indochina.

A recent finding providing possible insight regarding an earlier, intuitive research decision, it strikes me that this is a poweful, non-linear example of the value of visualization as an ongoing process, rather than a one-time production process that results in a specific finding. Additionally, this makes me ponder about cases where the reasons for one’s instincts and biases (in my case, the selection of ‘bombing’ as a target word) may sometimes be seen in the data.

Interactive Textplots

David’s ‘humanist‘ software was then used to create an interactive d3-based browser based on the textplot output gml for each corpus. Without Modularity Class coloring of the nodes, this ‘alpha’ interactive version is (also for the moment) less able to communicate the groups within the distribution of words than the static diagrams.

Kissinger Interactive Textplot (Telcons)

Kissinger Interactive Textplot (Memcons)




Topic Modeling Stream Graphs

The colored streams represent each of the 40 topics of the topic models created for the memcons (top) and the telcons (bottom). The pie graph at the right of each graph shows the relative proportion of topic weight for each month of correspondence. The difference in density between the memcons (which show more activity at the end of Kissinger’s tenure) and the telcons (which show more activity at the beginning) are explained in large part by his promotion to Secretary of State in 1974. Before that time, when he was National Security Advisor, Kissinger utilized telephone conversations to address most of the issues confronting him. After his promotion, he shifted to a more official forum of meetings and memoranda for most of his work.

This interactive diagram can be played back, and various months explored in more detail – for example, the largest spikes in the telcons and memcons correspond to the timing of Kissinger’s promotion to Secretary of State, and to meetings regarding the October 1973 Yom Kippur War and the resultant flurry of diplomatic activity to broker agreements between the combatants in May 1974.

Interactive Topic Model Stream Graphs


Topic Modeling Area Graphs

The capability to go beyond merely counting word frequency to measuring the correlations in frequency between words is a powerful tool for computational historical research. This technique, called ‘topic modeling,’ relies upon complex probabilistic mathematics beyond the capabilities of most historians. Using a variant of MALLET (open-source topic modeling software), I have assembled topic models of the Kissinger collections. The initial results of this process resulted in a 40-category list for both the memcons and telcons collections. By compiling the topic modeling data and graphing each topic’s frequency data into an x/y line/area graph, a contextual, historical timeline emerges for each of the 40 Kissinger memcon and telcon topics. Peaks in the graphs indicate the dates of documents that contain the highest cumulative ‘weighting,’ or relevance, to that respective topic. Immediately, the data graphed on the timeline evokes questions: many of the peaks on the topic graphs synchronize well with related events in the historical record. Examining each topic graph in relation to these historical timelines is in itself a useful exercise for researchers in finding content related to a particular topic.

For example, those interested in reading documents most closely associated with the wars in Indochina and Kissinger’s Paris Peace Conference talks with Le Duc Tho and Xuan Thuy, Chairman Mao and Chou En-lai, the Cambodia Campaign and resulting public outcry in May 1970, the ‘Backchannel’ and SALT talks with Dobrynin, Gromyko, Brezhnev, or other topic areas of Kissinger’s activity can use these graphs to locate the relevant dates and documentation for their topics much more easily than by consulting a traditional index.

Memcons: Interactive Topic Model Area Graphs

Telcons: Interactive Topic Model Area Graphs

Topic Modeling Force-Directed Graphs (Interactive)

Memcons: Interactive Topic Model Force Graph

The placement of the ‘Cambodia’ topic outside that military arc, much closer to ‘Laughter’ than, say, ‘Vietnam’ or ‘Soviet,’ is very interesting, suggesting that the archive may contain only those documents of a less contentious or generic nature compared to those other topics.The “Cambodia” topic’s comparative proximity to the Laughter topic, clearly visible in this graph, reflects an uncharacteristically ‘jovial’ slant of the content of the documents in the Cambodia topic in comparison to those from the other topics of similarly grave military importance. It is an odd result that supports other findings that the archive’s “Cambodia” material on which this topic is based is likely a hand-picked, sanitized and non-representative selection of only the more congenial exchanges regarding Cambodia, specifically excluding tense and difficult situations. Memoranda detailing planning and execution of disavowed military incursions, involvement in the installation of the Lon Nol regime, and other incidents are largely absent from the archive. Computational techniques here combined with a subjective historian’s assessment of the inapplicability of ‘laughter’ to topics like Cambodia, have thus uncovered a strong relationship between a document’s classification status and its subject matter. Further interpretations of the proximity of the ‘laughter’ topic (among others) to these geopolitical foci are detailed in greater depth in the written paper.

Telcons: Interactive Topic Model Force Graph
(NOTE: may take a while to load)


Topic Modeling performed using ‘MALLET Topic Modeling Toolkit.’

Topic Modeling Force-Directed Graphs (Static)

Instead of a more traditional x/y axis graph, each memcon in the archive and their relation to the 40 topics of the topic model are represented here using a ‘force-directed’ diagram. More than prior figures, this graph is off-putting at first and requires a bit of orientation to understand. Here each document is represented by one of a network of small circles, connected by lines and placed at a distance from the larger circles (the topics) according to their respective association to each topic. The size of the topic circles and their textual labels reflects the total weight afforded to them by the documents in the archive, and the color of the small documents’ circles and connecting lines reflects the classification status of each document.

Memcons: Static Topic Model Force Graph

This graph elegantly demonstrates in one view the interrelated ‘clusters’ of documents by proximity, their classification status, and the complex ways in which all documents relate to their constituent topic(s) and to one another. Even more than the line/area graphs, this image synthesizes the information gathered through metadata analysis, n-gram counting, and topic modeling to present inter-relationships not always readily apparent from a tabular view of the underlying data.

The blue dots/lines represent documents with ‘Top Secret’ classification status, the yellow dots are ‘Secret,’ the pink dots are ‘Unclassified’ and the 40 topics of the topic model are displayed as grey circles with text. Documents sharing similar topic weightings are clustered together, and placed at a relative distance from those topics. The placement of documents and topics related to matters of high military or national security significance among the bluish upper left region is unsurprising, as is the placement of ‘laughter’ so far on the other side of the graph. It is also notable that this upper left hand area of the graph contains those countries at the heart of Nixon and Kissinger’s vaunted “triangular diplomacy.” The topics concerning Soviet Union, China, Vietnam, and related topics are all placed in close proximity to one another occupying a close-knit area of the graph, suggesting that when those topics were mentioned they were often mentioned together. There is another fascinating topic in this topic model revealed by this graph, one with a unique significance. The “Laughter” topic is based upon those documents in which the transcriber literally placed the phrase “[laughter],” representing jovial, lighthearted moments of Kissinger’s correspondence in which the participants had a chuckle. A historian would expect these sorts of emotional expressions to occur in inverse proportion to the gravity of their respective topics (for example, the least ‘laughter’ during those negotiations in which relations were at their most sensitive, tense and/or adversarial), and the placement of the “Laughter” topic at the furthest possible point from topics relating to the Soviet Union, China and Vietnam negotiations validates this interpretation.

Word Collocation: Target Word ‘Bombing’

‘Bombing’ Word Collocation Force-Directed Graph

First and perhaps most strikingly, the MI score and the frequency of the words “Cambodia” and “Vietnam” in collocation with the word “bombing” differs greatly between the two channels of communication. When Kissinger and his associates were using the word ‘bombing’ in official meetings, it was associated much more with words related to ‘Vietnam’ than in the telephone conversations, in which ‘bombing’ was seen to have a higher MI score (collocation) with the names of other countries in Indochina (Laos, Thailand and Cambodia).

It is unsurprising that Kissinger would use the telephones (as National Security Advisor) as compared to formal meetings to discuss bombing in Indochina, given the differences in his expectations of privacy in those two different fora of conversation. However, more than just a quantitative representation of ‘candor,’ this difference may also suggest an absence of material – ‘Top Secret’ memcons on military aspects of the ‘Cambodia’ topic, for example.

‘Bombing’ Word Correlation Interactive Force-Directed Graph

This is an interactive ‘d3’ version of the force-directed word collocation analysis of the word ‘Bombing’. Currently, the diagram does not take ‘edge weights’ into account, so the nodes within each cluster are placed inexactly.

Until ‘edge weight’ code is completed, static graph above is far more accurate and ‘stable’.