
{"id":28,"date":"2009-01-28T12:00:40","date_gmt":"2009-01-28T12:00:40","guid":{"rendered":"http:\/\/opendna.com\/blog\/?p=28"},"modified":"2022-11-08T12:10:28","modified_gmt":"2022-11-08T12:10:28","slug":"catastrophic-frequencies","status":"publish","type":"post","link":"https:\/\/opendna.com\/blog\/2009\/01\/28\/catastrophic-frequencies\/","title":{"rendered":"Catastrophic Frequencies"},"content":{"rendered":"<h3><a name=\"cf-1\"><\/a>Introduction<\/h3>\n<p>Early communications researchers posited that the Culture Industries (Horkheimer &amp; Adorno 2006) and the Society of the Spectacle (Debord 1994) always reinforce power in the dominant paradigm. Via media and culture, the ideology of the mode of production impresses itself upon the minds, language and arts of society, consequently the ideology itself could be examined by studying its products.<\/p>\n<p>Mass culture requires (and thus creates) shared symbols, metaphors, myths, histories, and a common language which transcends mere agreement on definitions and forms of grammar. Because \u201cthe language and gestures of listeners and spectators are more deeply permeated by the patterns of the culture industry than ever before\u201d (Horkheimer, p.71) and \u201c[p]eople use words and expressions which they either have ceased to understand at all or use only according to their behavioral functions\u201d (ibid, p.70), it becomes necessary to investigate the process by which the culture industry transforms historic events into cultural units. One form identified by Horkheimer and Adorno is the bourgeois tragedy, \u201cincluded in society\u2019s calculations and affirmed as a moment of the world,\u201d (ibid, p.61) a \u201chouse of moral correction\u201d (ibid, p.62) and a \u201cthreat to destroy anyone who does not conform\u201d (ibid, p.61).<\/p>\n<p>The media record of the Bush Years (2000-2008), marked by several historic catastrophes (Harper, 2009), offers fertile ground for investigation of the creation of symbolic libraries in a mass culture where repetition of Signified failures of power become semantic tools of ideology. While the \u201cnuances (are) still beyond the reach of experimental methods\u201d (Horkheimer, p.71), this paper attempts to use crude machine-assisted word frequency to identify patterns indicating the commodification of a catastrophe into a tragic mythology.<\/p>\n<p>Roland Barthes\u2019 Mythologies describes a methodology for describing the construction of cultural units. Because the nomenclature is circular and recursive, an example can help illuminate what is here meant by signs, myths and mythologies.<\/p>\n<p>The phenomenon of written history in a literate society provides \u201can artificially extended and verifiable memory of objects and events not present to sight or recollection\u201d understood through \u201csymbols rather than things\u201d (Innis 1972, p.10). The construction of a people\u2019s common history requires general consensus upon the signs which signify historical events. When historical events are recorded in written form, the variety of signs used to signify must be reduced to those made available by the typographical technology of the language, before being further constrained to distinguish one event from another. Whereas photographs of soldiers raising a flag (Rosenthal 1945), children running from a fireball (Ut 1972) and a hooded man standing on a box (Unknown 2003) today signify both war generally but also their respective wars so clearly as to require no further context or explanation, the written sign \u201cwar\u201d must be augmented with prefixes and suffixes to distinguish one conflagration from another: \u201cWorld War II\u201d, \u201cVietnam War\u201d and \u201cIraq War\u201d, respectively. In other words, the technological demands of written script in the English language force the representation of historic events by a generally accepted sequence of alphanumeric characters.<\/p>\n<p>This observation opens the door to machine analysis of texts: \u201cWhen using units of analysis, such as words or other symbols, the computer can generate perfectly reliable frequency counts\u201d (West 2008, p.14). To the extent that the signification of historical events is limited to a set of recognizable signs, the computer can report the frequency that signified historical events are invoked.<\/p>\n<p>Frequency is well explored activity in the field of communication and \u201cwas a main activity of content analysis in the 1930s and 1940s\u201d (ibid, p. 16). Recognizing the challenges of making inferences from text when assuming that \u201cfrequency is a function of intensity\u201d (ibid, 2008) and therefore of importance, this paper equates the use of a sign in a text with the invocation of the historical event which the sign represents. Frequency over time is a measure of how often a historical event is invoked to explain the world, and is therefore a measure of the historical event\u2019s power as a culture\u2019s myth or mythology. Just as the Bombing of Pearl Harbor in 1941 was invoked to explain the 2001 terrorist attacks on the World Trade Center in New York, so too will more recent history be invoked to explain future news cycles.<br \/>\n<!--nextpage--><\/p>\n<h3><a name=\"cf-2\"><\/a>Hypothesis of Catastrophic Frequencies<\/h3>\n<p>If a sign pre-exists the catastrophic event it later comes to signify, it is likely to occur with low frequency prior to the event relative to the news cycle concurrent with the catastrophe. The more time passes, from the moment of the catastrophe, the less newsworthy the event will be regarded and the less frequently the sign will occur. It is likely that all major catastrophes will be revisited on the first (and possibly subsequent) anniversaries, which will be reflected in a spike in frequencies. If a catastrophe has been elevated to the status of myth or mythology, it is likely that it will be invoked at a regular rate significantly greater than pre-signification.<\/p>\n<p>From these expectations, five phenomena can be defined as the Background, the Catastrophe, the Aftermath, the Latency and the Memorials:<\/p>\n<ul>\n<li><strong>Background<\/strong> \u2013 a pre-mythic period (1) prior to the Singularity, (2) in which the Signifier and Signified may exist independently, (3) measurable word frequencies establish a baseline for comparison.<\/li>\n<li><strong>Catastrophe<\/strong> \u2013 the catastrophic moment which alters the physical environment: (1) media coverage of the event names the event, attaching the Signifier to the Signified, (2) word frequency spikes to orders of magnitude over the Background.<\/li>\n<li><strong>Aftermath<\/strong> \u2013 political, social, economic and psychological recovery from the catastrophe in which a mythology is constructed. Marked by (1) a search for meaning, (2) the assignment of blame, (3) a search for lessons, (4) the re-branding by application of new Signifiers, (5) word frequencies decline geometrically.<\/li>\n<li><strong>Latency<\/strong> \u2013 a new post-catastrophe baseline is established as the mythology enters the cultural symbolic library. Marked by (1) use of the Signifier without discussion of the Signified, (2) regular word frequency sets a new baseline above that of the Background.<\/li>\n<li><strong>Memorials<\/strong> \u2013 a periodic re-visitation of the catastrophe and the mythos (1) during anniversaries, or (2) to lend the mythology to similar sub-catastrophic events.<\/li>\n<\/ul>\n<p><!--nextpage--><\/p>\n<h3><a name=\"cf-3\"><\/a>Brief History of the Method<\/h3>\n<p>Early machine-assisted content analysis was hampered by the high costs of processing power and data storage. In 1976, DeWeese published two \u201ctechnological feasibility studies of computer analysis of media content\u201d which concluded that \u201ca large-scale project would cost $3 million per billion words\u201d or about $0.30\/word (West, p. 19). DeWeese further found that document scanners capable of optical character recognition (OCR) cost upwards of $1.3 million (ibid.). In the late 1960s and early 1970s, mainframe time cost around $75 an hour (Stewart 2007).<\/p>\n<p>Advances in computer technology, mass commercialization of personal computers and the development of \u201conline information services that provide the full text of documents in digital form, make computer-assisted content analysis more accessible and practical now than ever before.\u201d (West, p. 15) While consumer-grade OCR technologies are now available for less than $300 (Berline 2008), information services like LexisNexis and Canadian Newsstand have made OCR largely unnecessary for most newspaper content analysis projects. The post-millennial proliferation of UNIX-clone operating systems (e.g. Linux, FreeBSD and MacOS X) has made it possible to run mainframe applications on laptops and thus lowered the barriers to the use of powerful Unix shells.<\/p>\n<p>These technological developments have, as with machine translation, lent support to post-Structuralism linguistic criticism of machine-assisted content analysis: computers still do not understand human language. Computers cannot differentiate homographs, process unrecognized phrases or symbols, parse metaphors, or make judgments or interpretations unless specifically programmed to do so (West, p.15). Many efforts have been made to overcome these limitations and lexicological software like The General Inquirer (Stone, 1966), which relies on pre-coded dictionaries to score and compare texts, have made substantial progress (Buvac, n.d.) and enjoy continued application (Lim 2008; Hall 2005).<\/p>\n<p>Ironically, the acceleration of communication (and communication as entertainment) facilitated by \u201cconsumer mainframes\u201d and global information networks has widened one of the holes in pre-coded dictionaries. Communities and world events coin new phrases, describe new symbols and invent new myths at an accelerated rate, so that phrases like \u201cWorld Trade Center\u201d may be included in a lexical database like WordNet (Miller et al., 2009) without weight for the emotion the words now evoke, and slang like \u201cjumped the shark\u201d1 or \u201cepic fail\u201d2 may be omitted entirely, at least until the next version is released.<\/p>\n<p>In examining the word frequencies of several catastrophes, this paper explores one possible avenue open to identifying a specific class of myths created by contemporary events: sudden calamities, or \u201ccatastrophes\u201d. Some of the catastrophes examined have indisputably entered the realm of myth (if not mythology), and others probably never came close. While there has been a great deal of innovation in the field of infographics in recent years (e.g. Appendix C), this study limits the illustration of frequency to single and multi-variable longitudinal line and area graphs.<br \/>\n<!--nextpage--><\/p>\n<h3><a name=\"cf-4\"><\/a>Method<\/h3>\n<p>The source data for this study consisted of all articles published in Section A (the front-section) of the New York Times (NYT) for each of the ninety-six (96) months from January 2001 to December 2008 (inclusive). The New York Times was selected because of its \u201cgenerally recognized influence on other media\u201d (West, p.8).<\/p>\n<p>Month-by-month searches of the Lexus-Nexus database of NYT articles limited the selection to \u201cSection A\u201d and excluded \u201cpaid notices\u201d. Search results were downloaded in plain text for maximum compatibility with analysis softwares. Because the typical search returned between 2000 and 2500 articles per month, and Lexus-Nexus limits downloads to 500 article-batches, raw data for each month consisted of four to five separate text files. The mean hard disk use was just over seven (7) Megabytes (MB) per month or 87 MB per year, for a total of 700 MB over the eight-year period.<\/p>\n<p>During initial trials, TextSTAT (H\u00fcning 2008) was run on each month to produce word frequency counts for \u201cHurricane Katrina\u201d and \u201cKatrina\u201d, \u201c9\/11, \u201cIraq\u201d and \u201cfail\u201d. Results were verified using TextSTAT\u2019s keyword-in-context (KWIC) tool.3 These trials demonstrated that TextSTAT is capable of (1) combining several data files into a single \u2018corpus\u2019, (2) rapidly producing KWIC lists from large data sets, (3) producing accurate frequency lists, and (4) exporting results in widely-compatible file formats. While TextSTAT agilely manipulates a single document, the specific demands of this project required a search for alternative software: (a) the process for producing word frequency lists is processor (and time) intensive, requiring as much as an hour per month; (b) exported KWIC lists do not list tallies and are ill-suited for spreadsheets; and (c) there is no scripting or job-queuing feature.<\/p>\n<p>In the course of evaluating competing applications which could produce word frequency lists, it was discovered that the problem is a staple of Programming 101 texts. The widely-published wf.sh and wf2.sh bash scripts (Cooper 2007) apparently originated with a challenge issued to Don Knuth by John Bentley in (Bentley, May 1986, p.368): \u201cGiven a text file and an integer K, you are to print the K most common words in the file (and the number of their occurrences) in decreasing frequency.\u201d Doug McIlroy offered a six-line solution for Unix systems (Bentley, June 1986, p.471):<\/p>\n<ol>\n<li>Replace all spaces with line breaks and remove duplicate line breaks<\/li>\n<li>Transliterate upper case to lower case<\/li>\n<li>Sort alphabetically to bring identical words together<\/li>\n<li>Replace each run of duplicate words with a single representative and include a count<\/li>\n<li>Sort in reverse numeric order<\/li>\n<li>Print K lines<\/li>\n<\/ol>\n<p>Initial tests run revealed that, in just seconds, a McIlroy-derived wf.sh produced word frequency lists similar to TextSTAT. Errors in the wf.sh-produced frequency lists primarily resulted from the inability to parse punctuation (i.e. words were counted separately if co-joined by a comma or period) and non-printing ASCII characters presents in Lexis-Nexis exports. Using loops and standard Unix commands, a daisy-chain of scripts (based on wf.sh) was written to process the data files and produce longitudinal keyword frequency information:<\/p>\n<p>catit.sh: for each month in each year<\/p>\n<p>Combine the multiple downloaded data files by month ($year$mo.txt)<\/p>\n<ol>\n<li>Output to a working directory<br \/>\nformat.sh: for each file in the working directory<\/li>\n<li>Transliterate upper case to lower case letters<\/li>\n<li>Harmonize non-printable ASCII characters<\/li>\n<li>Transliterate common multi-word signs to single words (i.e. \u201cNew York\u201d to \u201cnewyork\u201d)<\/li>\n<li>transliterate all punctuation to spaces<br \/>\ncount.sh: for each file<\/li>\n<li>Sort alphabetically to bring identical words together<\/li>\n<li>Replace each run of duplicate words with a single representative and include a count<\/li>\n<li>Sort in reverse numeric order<\/li>\n<li>Print a list for each month in each year<br \/>\nwords.sh: for each defined keyword<\/li>\n<li>Search within each month-year list for the keyword<\/li>\n<li>Output the line including keyword, count and month-year<\/li>\n<\/ol>\n<p>The initial seven keywords defined in words.sh were chosen as representative of generally-recognizable catastrophes or events of the 2001-2008 period:<\/p>\n<p>Enron \u2013 a multinational energy-trading corporation with strong political ties to President Bush, which was accused for causing the California Energy Crisis (June 2001) and became one of the largest bankruptcies in American history (December 2001).<\/p>\n<ul>\n<li><strong>Tsunami<\/strong> \u2013 the December 26th 2004 Indian Ocean earthquake which released a tsunami resulting in one of the deadliest natural disasters in recorded history.<\/li>\n<li><strong>Katrina, Hurricane<\/strong> \u2013 the 2005 Atlantic hurricane which made landfall in northeast Louisiana (USA), resulting in storm surges which collapsed and flooded the city of New Orleans, and became one of the costliest natural disasters in American history.<\/li>\n<li><strong>9\/11<\/strong> \u2013 the September 11 2001 terrorist attacks against the New York World Trade Centers, which caused the WTC towers to collapse and is widely regarded as one of the worst terrorist attacks in history.<\/li>\n<li><strong>Iraq<\/strong> \u2013 the nation against which the United States launched a military invasion in March 2003, ending the ceasefire which ended the Gulf War (1990-1991).<\/li>\n<li><strong>Abu Ghraib<\/strong> \u2013 the Iraqi prison which was used to torture enemies of the Baathist regime prior to the US invasion in 2003, and was revealed to be one location where US soldiers tortured Iraqi detainees.<\/li>\n<\/ul>\n<p>Because the program\u2019s default output would fail to distinguish between \u201c9\/11\u201d, the sign for a terrorist attack, and \u201c911\u201d, the phone number for emergency services in most of North America, a series of transliterations were coded into format.sh(5) to distinguish the two. Some further additions to format.sh(5) aimed to transliterate \u201cSeptember 11th\u201d with \u201c9\/11\u201d while excluding newspaper datelines which might otherwise inflate word frequencies during the month of September.<\/p>\n<p>The resulting data files were then manually examined for errors and inappropriate conjunctions, corrected and harmonized to the queried keywords. A data table of these results is included in Appendix A. In the following section the notation {word} is used to denote a keyword and its variants. For example, {WTC} denotes both \u201cWTC\u201d and \u201cWorld Trade Center\u201d. Ordinals followed by \u201cwf\u201d denote word frequency counts.<br \/>\n<!--nextpage--><\/p>\n<h3><a name=\"cf-5\"><\/a>Findings &amp; Conclusions<\/h3>\n<p><strong>{ENRON} and {TSUNAMI}<\/strong><br \/>\n{ENRON} and {TSUNAMI} resulted in longitudinal keyword frequencies in line with the expectation for non-mythical catastrophes. At their time, each was a disaster worthy of history books: the collapse of Enron was the largest bankruptcy in American history and the \u201cBoxing Day Tsunaimi\u201d was the most deadly natural disaster in modern history. The New York Times gave each substantial attention during the Catastrophe phase and some attention during the Aftermath, but neither had an appreciable Latency beyond the first Memorial.<\/p>\n<p>Enron had been high-profile, media-savvy, politically connected corporation prior to its catastrophic bankruptcy. Prior to revelations of its economic difficulties in November 2002, {ENRON} had a mean 16wf. This rose to 1019wf during the four-month Catastrophe, falling to an average 178wf in the one-year Aftermath, and to 34wf and 22wf in the following years. {ENRON}\u2019s word frequencies rose again (twice past 100wf) between December 2005 and May 2006, during the criminal trial of founder Ken Lay and CEO Jeff Skilling.<\/p>\n<p>{TSUNAMI} received 906 hits December 2005-January 2006 and had a slightly higher latency. The 12-month Background had 11 of 12 months with fewer than 3 hits and the Latency period witnessed 8 of 12 months with 30 hits or fewer. A more detailed content analysis is required to identify the source of this increase, but a haphazard sample of articles in July 2006 (the last spike) reveals news of a smaller tsunami, a reference to the tsunami relief efforts of former Presidents Bush and Clinton within the context of solving Israel\/Palestine conflicts, and criticism of the reconstruction efforts of aid agencies leveled by President Clinton.<br \/>\n<strong>{HURRICANE} and {KATRINA}<\/strong><br \/>\nIn contrast to the first two examples, the drowning of New Orleans in 2006 illustrated some of the features expected of a catastrophic myth. Because a Sign was pre-assigned by authorities (like Enron) prior to the apex of the Catastrophe, there was never any doubt as to what to call Hurricane Katrina (unlike the tsunami). Unsurprisingly, there was a high degree of concordance between {HURRICANE} and {KATRINA} during the Hurricane Katrina catastrophe and in subsequent newspaper articles. It was also clear, however, that the background pattern of use is disrupted after September 2005.<\/p>\n<p>In the {HURRICANE} graph we saw five distinct spikes in September of 2003, 2004, 2005, and 2007 and in August of 2007. Each of these spikes in word frequency corresponded to the American Atlantic \u201churricane season\u201d. In 2003, Hurricane Isabel made news more for hitting the Washington DC metropolitan area than for her destructive power, while 2004 witnessed a convergence of Hurricane Ivan \u201cthe Terrible\u201d and US presidential candidates campaigning in a storm-ravaged swing-state (Florida). The catastrophes in 2003 and 2004 had short Aftermaths and word frequencies quickly returned to Background levels, a pattern that was clearly broken in 2005 with Hurricane Katrina.<\/p>\n<p>In both the {HURRICANE} and {KATRINA} graphs, we saw a long Aftermath and a long latency above Background levels. The 10-month Background for {HURRICANE}, between Ivan and Katrina, had a mean 57wf, which rose to 232 in the 12-month Aftermath, and 65wf and 56wf for each of the following years (excluding spikes for 2007 and 2008 hurricane seasons). The 12-month Background for {KATRINA} had a mean 0.67wf versus 130wf for the 12-month Aftermath, and a mean 56wf and 44wf for each of the following two years.<\/p>\n<p>The frequency of \u201churricane\u201d remained elevated from two per day to over seven per day until the first anniversary of Hurricane Katrina, after which it returned to Background levels. The frequency of \u201cKatrina\u201d, on the other-hand, remained elevated from once every-other month to four per day for the Aftermath, and remained elevated at a level equal to \u201churricane\u201d for the following two years. Haphazard samples suggest that every hurricane was compared to Katrina, that Katrina was often politically invoked as a failure or challenge independently from \u201churricane\u201d (Krugman 2007). The September 2008 spike in \u201cKatrina\u201d was almost certainly the result of Hurricane Gustav (an otherwise unremarkable storm) coinciding with the Republican National Convention.<br \/>\n<strong>Terrorist Attacks of September 11th 2001<\/strong><br \/>\nEach of the four catastrophes so far discussed varied in the details of their profiles, but collectively hold to the expected form. The initial query for the terrorist attacks of September 11th 2001 \u2013 most commonly termed \u201c9\/11\u201d \u2013 revealed an unexpected deviation from this form.<\/p>\n<p>According to the {9\/11} graph, there was a long build-up to the first peak4 which did not coincide with the actual attacks. The six peaks occur in September 2002, 2003, 2005, 2006 (the anniversaries of the attacks), and between March and October 2004 (the 2004 US Presidential campaign season). While the occurrence of anniversaries and the strong Latency evident in {9\/11} fits with the expectations, the slow build-up to the first peak suggests other keywords needed to be investigated.<\/p>\n<p>Further queries were run for {OSAMA}, {BIN LADEN}, {AL QAEDA}, {TERRORIST ATTACK} and {WTC}, all signs commonly associated with the catastrophe. The results of these queries all fit with the general expectation of catastrophic events. The latter three terms fit with elevated Latency expected of myth-level catastrophes while the former two probably do not. {OSAMA} and {BIN LADEN} had higher background levels for (15wf and 43wf) compared to the other terms (9wf, 7wf, 5wf) and a longer study period may reveal that the first two terms were already mythic, owing perhaps to the truck bomb attack on the World Trade Center in 1993.<\/p>\n<p>As with {9\/11}, {OSAMA} and {BIN LADEN} saw anniversaries in 2002, 2004 and 2006. There is, however, a conspicuous absence of substantial anniversaries in 2003 and 2005. The sustained the increase in frequency during the 2004 US Presidential campaign season and peaks during anniversaries which coincide with mid-term elections suggests the terms {OSAMA} and {BIN LADEN} are related to election activity.<\/p>\n<p>{WTC} and {TERRORIST ATTACK} showed expected catastrophic peaks, Aftermaths, and substantial Latencies. Recognizing that {9\/11} was synonymous with the {TERRORIST ATTACK} on the {WTC}, it was worth returning to Barthe\u2019s Mythologies: load latter two signs into the first and construct the myth {9\/11}. This was graphed with a \u201cstacked area graph\u201d which displays the sum of the word frequencies while keeping the individual terms visually distinct.<\/p>\n<p>In this graph we saw Background levels as negligible, and the moment of the catastrophe in the first peak of {WTC} and {TERRORIST ATTACK}. With the first anniversary we witnessed a peak of all three terms and the beginning of the rebranding of the event into {9\/11}. By the 2004 US Presidential campaign {9\/11} substantially outnumbered both of the other two terms, was clearly the favored term for the catastrophe, and suggested that the event has passed into myth.<\/p>\n<h3><a name=\"cf-6\"><\/a>Conclusion<\/h3>\n<p>The findings support the hypothesis that the explanatory power of an event would be found not in the word frequency of the catastrophe, but in the \u201clatency\u201d of the word frequency over long periods. The example of \u201c9\/11\u201d demonstrates the myth-creation process described by Barthes, but warns of the misleading results that can be arrived at with inadequate attention to keyword selection. The co-incidence of periods of high-word frequency with periods of political importance (like presidential and mid-term election seasons) suggests that studies of concordance may find significance, and that similar patterns may be observable for other myths. The graph for {IRAQ} in Appendix B, for example, more closely resembles {9\/11} than any other query, suggesting that multiple variables were driving word frequency. While the familiar 2004 US presidential election-season spike appeared to be present, the earlier 2002-2003 period resembles \u201cFalse Statements by Month\u201d graph (Appendix C) produced by the Center for Public Integrity (Lewis &amp; Reading-Smith, 2008). If further studies consistently link myth-like word frequencies with presidential communication they may provide quantitative support for The Rhetorical Presidency and its thesis of a \u201cNewsmaker-in-Chief\u201d (Friedman, 2007).<\/p>\n<h3><a name=\"cf-notes\"><\/a>Notes<\/h3>\n<ol>\n<li class=\"notes\">Queries for \u201cJumped the shark\u201d or \u201cjump the shark\u201d resulted in over 91,000 hits on Google Blog Search and 900 hits on a LexisNexis query of major US and world publications and newswires.<\/li>\n<li class=\"notes\">Queries for \u201cepic fail\u201d resulted in over 114,000 hits on Google Blog Search and 53 hits on a LexisNexis query of major US and world publications and newswires.<\/li>\n<li class=\"notes\">This exercise revealed that Hurricane Katrina is still mentioned an average of once a day even three years after the event, that 10% of those occurrences overtly reference failure. While such a concordance is likely significant, a robust statistical method would be required confidently express significance with weaker keyword pairs.<\/li>\n<li class=\"notes\">The term \u201ccatastrophe\u201d is avoided here because it denotes a sudden calamity, and suddenness is not represented in the {9\/11} graph.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>A longitudinal study of keyword frequencies in New York Times between 2001 and 2008 supported the hypothesized typologies of catastrophic myths. Patterns of occurrence are consistent between natural and man-made disasters. <\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"webmentions_disabled_pings":false,"webmentions_disabled":false,"activitypub_content_warning":"","activitypub_content_visibility":"","activitypub_max_image_attachments":3,"activitypub_interaction_policy_quote":"anyone","activitypub_status":"","footnotes":""},"categories":[210],"tags":[113,114,115,73,116,117,28,118,40,74,119,120,121,122,123,124,125,17,43,51,126,127,42,128,129,44,130,131,97,45,23,102],"class_list":["post-28","post","type-post","status-publish","format-standard","hentry","category-essays","tag-analysis","tag-catastrophes","tag-catastrophic-frequencies","tag-disaster","tag-don-knuth","tag-doug-mcilroy","tag-energy","tag-experimental-methods","tag-history","tag-hurricane-katrina","tag-john-bentley","tag-kwic","tag-lexisnexis","tag-longitudinal","tag-metaphors","tag-method","tag-newspaper","tag-politics","tag-programming","tag-research","tag-roland-barthes","tag-roland-barthes-mythologies","tag-scripting","tag-semantic-tools","tag-september-11-attacks","tag-software","tag-textstat","tag-tsunami","tag-united-states","tag-unix","tag-usa","tag-word-frequency"],"_links":{"self":[{"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/posts\/28","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/comments?post=28"}],"version-history":[{"count":1,"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/posts\/28\/revisions"}],"predecessor-version":[{"id":1843,"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/posts\/28\/revisions\/1843"}],"wp:attachment":[{"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/media?parent=28"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/categories?post=28"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/opendna.com\/blog\/wp-json\/wp\/v2\/tags?post=28"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}