Using Computerized Text Analysis to Assess Threatening Communications and Behavior

Cindy K. Chung and James W. Pennebaker


Understanding the psychology of threats requires expertise across multiple domains. Not only must the actions, words, thoughts, emotions, and behaviors of the person making a threat be examined, but the world of the recipient of the threats also needs to be understood. The problem is more complex when considering that threats can be made by individuals or groups and can be directed toward individuals or groups. A threat, then, can occur across any domain and on multiple levels and must be understood within the social context in which it occurs.

Within the field of psychology, most research on threats has focused on the nonverbal correlates of aggression. In the animal literature, for example, considerable attention has been paid to behaviors that signify dominance or submission. Various species of birds, fish, and mammals often change their appearance by becoming larger when threatening others. Dominance and corresponding threat displays have also been found in vocalization, gaze, and even smell signals (e.g., Buss, 2005). In the literature on humans, an impressive number of studies have analyzed threatening behaviors by studying posture, facial expression, tone of voice, and an array of biological changes (Hall et al., 2005).

The authors wish to acknowledge funding from the Army Research Institute (W91WAW-07-C-0029), CIFA (DOD H9c104-07-C-0014), NSF (NSF-NSCC-090482), DIA (HHM-402-10-C-0100), and START (DHS Z934002). They would also like to thank Douglas H. Harris, Cherie Chauvin, and Amanda Schreier for their helpful comments in the preparation of the manuscript.



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Using Computerized Text Analysis to Assess Threatening Communications and Behavior Cindy K. Chung and James W. Pennebaker U nderstanding the psychology of threats requires expertise across multiple domains. Not only must the actions, words, thoughts, emotions, and behaviors of the person making a threat be exam- ined, but the world of the recipient of the threats also needs to be under- stood. The problem is more complex when considering that threats can be made by individuals or groups and can be directed toward individuals or groups. A threat, then, can occur across any domain and on multiple lev- els and must be understood within the social context in which it occurs. Within the field of psychology, most research on threats has focused on the nonverbal correlates of aggression. In the animal literature, for example, considerable attention has been paid to behaviors that signify dominance or submission. Various species of birds, fish, and mammals often change their appearance by becoming larger when threatening oth- ers. Dominance and corresponding threat displays have also been found in vocalization, gaze, and even smell signals (e.g., Buss, 2005). In the liter- ature on humans, an impressive number of studies have analyzed threat- ening behaviors by studying posture, facial expression, tone of voice, and an array of biological changes (Hall et al., 2005). The authors wish to acknowledge funding from the Army Research Institute (W91WAW- 07-C-0029), CIFA (DOD H9c104-07-C-0014), NSF (NSF-NSCC-090482), DIA (HHM-402- 10-C-0100), and START (DHS Z934002). They would also like to thank Douglas H. Harris, Cherie Chauvin, and Amanda Schreier for their helpful comments in the preparation of the manuscript. 3

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4 THREATENING COMMUNICATIONS AND BEHAVIOR Although nonverbal features of threats are clearly important, many of the most dangerous threats between people are conveyed using language. Whether among individuals, groups, or entire nations, early threats often involve one or more people using words to warn others. Despite the obvi- ous importance of natural language as the delivery system of threats, very few social scientists have been able to devise simple systems to identify or calibrate language-based threats. Only recently, with the advent of com- puter technology and the availability of large language-based datasets, have scientists been able to start to identify and understand threaten- ing communications and responses to them through the study of words (Cohn et al., 2001; Pennebaker and Chung, 2005, 2008; Smith, 2004, 2008; Smith et al., 2008). This paper provides a general overview of computerized language assessment strategies relevant to the detection and assessment of word- based threats. It is important to appreciate that this work is in its infancy. Consequently, there are no agreed-on methods or theories that have defined the field. Indeed, the “field” is currently made up of a small group of laboratories generally working independently with very different back- grounds and research goals. The current review explores threats from a decidedly social-psychological perspective. As such, the emphasis is on the ways in which word use can reveal important features of a threatening message and also the psychological nature of the speaker and the target of the threatening communication. Whereas traditional language analyses have emphasized the content of a threatening communication (i.e., what the speaker explicitly says), this review focuses on the language style of the message, especially those words that people cannot readily manipulate (for a review, see Chung and Pennebaker, 2007). This is especially helpful in the area of assessing threatening communications and actual behavior because subtle markers of language style (e.g., use of pronouns or articles) can reveal behavioral intent that the speaker may be trying to withhold from the target. Finally, this paper discusses methods that have the goal of automated analyses and largely draws on word count approaches, which are increasingly being used in the social sciences. Computerized tools are especially help- ful for establishing a high standard of reliability in any given analysis and for real-time or close to real-time assessment of threatening communica- tions, so that our analyses might one day lead to interventions as opposed to just retrospective case studies. This paper also briefly describes common automated methods avail- able to study language content and language style. Next, a classification scheme for different types of threats is presented that serves as the orga- nizing principle for this review. The next section summarizes empirical research that has been conducted to assess intent and actual behaviors in

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5 USING COMPUTERIZED TEXT ANALYSIS contexts of varying stakes using text analysis. The review concludes with a discussion of the gaps where research is desperately needed across vari- ous fields, along with our perspective on how to improve predictions and an emphasis on how various models should be built and applied. TEXT ANALYSIS METHODS Features of language or word use can be counted and statistically ana- lyzed in multiple ways. The existing approaches can be categorized into three broad methodologies: (1) judge-based thematic content analysis, (2) computerized word pattern analysis, and (3) word count strategies. All are valid approaches to understanding threatening communications and can potentially yield complimentary results to both academic and nonacademic investigators. While it is beyond the scope of this paper to review each approach in detail, an overview is given below. Then the discussion focuses on word count strategies, which serve as the basis for the remainder of the review. Judge-Based Thematic Content Analysis Qualitative approaches use an expert or a group of judges to system- atically rate particular texts along various themes. Such approaches have explored the subjective or psychological meaning of language within a phrase or sentence (e.g., Semin et al., 1995), conversational turn (e.g., Tannen, 1993), or an entire narrative (e.g., McAdams, 2001). Thematic content analyses have been widely applied for studying a variety of psychological phenomena, such as motive imagery (e.g., Atkinson and McClelland, 1948; Heckhausen, 1963; Winter, 1991), explanatory styles (Peterson, 1992), cognitive complexity (Suedfeld et al., 1992), psychiatric syndromes (Gottschalk et al., 1997), and goal structures (Stein and Albro, 1997). Several problems exist with qualitative approaches to text analysis. Judge-based coding requires elaborate coding schemes, along with mul- tiple trained raters. The reliability of judges’ ratings must be assessed and reevaluated early in the process through extensive discussions. Consider- ation of time and effort has limited analyses of this kind to small numbers of individuals per analysis. For the analysis of completely open-ended text, for example, when a series of very different threatening communi- cations are assessed for the probability of leading to actual threatening behaviors, the coding schemes developed in judge-based thematic content analysis may not be applicable or particularly relevant to any new threat or document. As a side note, the authors have spoken with and read about a number

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6 THREATENING COMMUNICATIONS AND BEHAVIOR of “expert” language analysts who often market their own language anal- ysis methods. Some of these approaches claim to reliably assess deception, author identification, or other intelligence-relevant dimensions. Often, it is claimed that the various methods have accuracy rates of more than 90 to 95 percent. To our knowledge, no human-based judge system has ever been independently assessed by a separate laboratory or been tested outside of experimentally produced and manipulated stimuli. Given the current state of knowledge, it is inconceivable that any language assess- ment method—whether by human judges or the best computers in the world—could reliably detect real-world deception or other psychological quality at rates greater than 80 percent, even in highly controlled datasets. This issue will be discussed in greater detail later. Computerized Word Pattern Analysis Rather than exploring text “top down” within the context of previ- ously defined psychological content dimensions, word pattern strate- gies mathematically detect “bottom up” how words covary across large samples of text (Foltz, 1996; Poppin, 2000) or the degree to which words overlap within texts (e.g., Graesser et al., 2004). One particularly promis- ing strategy is Latent Semantic Analysis (LSA; see, e.g., Landauer and Dumais, 1997), which is a method used to learn how writing samples are similar to one another based on how words are used together across docu- ments. For example, LSA has been used to detect whether or not a student essay has hit all the major points covered in a textbook or the degree to which a student essay is similar to a group of essays previously graded with top grades on the same topic (e.g., Landauer et al., 1998). Not only can word pattern analyses detect the similarity of groups of text, they can also be used to extract the underlying topics of text sam- ples (see Steyvers and Griffiths, 2007). One example of a topic modeling approach in the social sciences is the Meaning Extraction Method (MEM; Chung and Pennebaker, 2008). MEM finds clusters of words that tend to co-occur in a corpus. The clusters tend to form coherent themes that have been shown to produce valid dimensions for a variety of corpora. For example, Pennebaker and Chung (2008) found MEM-derived word factors of al-Qaeda statements and interviews that differentially peaked during the times when those topics were most salient to al-Qaeda’s mis- sions. MEM-derived factors have been shown to hold content validity across multiple domains. Since the MEM does not require a predefined dictionary (only characters separated by spaces), and translation occurs only at the very end of the process, MEM has served as an unbiased way to examine psychological constructs across multiple languages (e.g., Ramirez-Esparza et al., 2008, in press; Wolf et al., 2010a, 2010b).

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7 USING COMPUTERIZED TEXT ANALYSIS Word pattern analyses are generally statistically based and therefore require large corpora to identify reliable word patterns (e.g., Biber et al., 1998). Some word pattern tools feature modules developed from discourse processing, linguistics, and communication theories (e.g., Crawdad Tech- nologies1; Graesser et al., 2004), representing a combination of top-down and bottom-up processing capabilities. Overall, word pattern approaches are able to assess high-level features of language to assess commonalities within a large group of texts. Word Count Strategies The third general methodology focuses on word count strategies. These strategies are based on the assumption that the words people use convey psychological information over and above their literal mean- ing and independent of their semantic context. Word count approaches typically rely on a set of dictionaries with precategorized terms. The cat- egories can be grammatical categories (e.g., adverbs, pronouns, preposi- tions, verbs) or psychological categories (e.g., positive emotions, cognitive words, social words). While grammatical categories are fixed (i.e., entries belong in one or multiple known categories), psychological categories are formed by judges’ ratings on whether or not each word belongs in a category. Computerized software can then be programmed to categorize words appearing in text according to the dictionary that it references. Accordingly, these programs typically allow for the use of new, user- defined dictionaries, enabling broader or more specific sampling of word categories. Today, there is an ever-increasing number of applications of word count analyses in clinical psychology (e.g., Gottschalk, 1997), criminol- ogy and forensic psychology (e.g., Adams, 2002, 2004), cultural and cross-language studies (e.g., Tsai et al., 2004), and personality assess- ments (e.g., Pennebaker and King, 1999; Mehl et al., 2006). An increas- ingly popular tool used for text analysis in psychology is Linguistic Inquiry and Word Count (LIWC; Pennebaker et al., 2007). LIWC is a computerized word counting tool that searches for approximately 4,000 words and word stems and categorizes them into grammatical (e.g., articles, numbers, pronouns), psychological (e.g., cognitive, emotions, social), or content (e.g., achievement, death, home) categories. Results are reported as a percentage of words in a given text file, indicating the degree to which a particular category was used. The words in LIWC categories have previously been validated by independent judges, and 1 Find Crawdad text analysis software at http://www.crawdadtech.com, Crawdad Tech- nologies LLC [April 2010].

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8 THREATENING COMMUNICATIONS AND BEHAVIOR use of the categories within texts has been shown to be a reliable marker for a number of psychologically meaningful constructs (Pennebaker et al., 2003; Tausczik and Pennebaker, 2010). Using LIWC, word counts have been shown to have modest yet reliable links to personality and demographics. For example, one study across 14,000 texts of varying genres found that women tend to use more personal pronouns and social words than men and that men tend to use more articles, numbers, and fewer verbs (Newman et al., 2008). Together, these findings suggest that women are more socially oriented and that men tend to focus more on objects. Word count tools have effectively uncovered psychological states from spoken language (e.g., Mehl et al., 2006), in published literature (e.g., Pennebaker and Stone, 2003), and in computer-mediated communications (e.g., Chung et al., 2008; Oberlander and Gill, 2006). There is also evidence that word counts are diagnostic of various psychiatric disorders and can reflect specific psychotic symptoms (Junghaenel et al., 2008; Oxman et al., 1982). For example, Junghaenel and colleagues found that psychotic patients tend to use fewer cognitive mechanism and communication words than do people who are not suf- fering from a mental disorder, reflecting psychotic patients’ tendencies to avoid in-depth processing and their general disconnect from social bonds. These studies provide evidence that word use is reflective of thoughts and behaviors that characterize psychological states. Word counts provide meaningful measures for a variety of thoughts and behaviors. LANGuAGE CONTENT VERSuS LANGuAGE STYLE Most early content analysis approaches by both humans and comput- ers focused on words related to specific themes. By analyzing an open- ended interview, a human or computer can detect theme-related words such as family, health, illness, and money. Generally, these words are nouns and regular verbs. Nouns and regular verbs are “content heavy” in that they define the primary categories and actions dictated by the speaker or writer. It makes sense; to have a conversation, it is important to know what people are talking about. However, there is much more to communication than content. Humans are also highly attentive to the ways in which people convey a message. Just as there is linguistic content, there is also linguistic style— how people put their words together to create a message. What accounts for “style”? Consider the ways by which three different people might summarize how they feel about ice cream:

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9 USING COMPUTERIZED TEXT ANALYSIS Person A: I’d have to say that I like ice cream. Person B: The experience of eating a scoop of ice cream is certainly quite satisfactory. Person C: Yummy. Good stuff. The three people differ in their use of pronouns, large versus small words, verbosity, and other dimensions. We can begin to detect linguistic style by paying attention to “junk words”—those words that do not con- vey much in the ways of content (for a review, see Chung and Pennebaker, 2007; Pennebaker et al., 2003). These junk words, usually referred to as function words, serve as the cement that holds the content words together. In English, function words include pronouns (e.g., I, they, it), prepositions (e.g., with, to, for), articles (e.g., a, an, the), conjunctions (e.g., and, because, or), auxiliary verbs (e.g., is, have, will), and a limited number of other words. Although there are less than 200 common function words, they account for over half of the words used in everyday speech. Function words are virtually invisible in daily reading and speech. Even most language experts could not tell if the past few paragraphs have used a high or low percentage of pronouns or articles. People are reliable in their use across contexts and over time. Although most everyone uses far more pronouns in informal settings than in formal ones, the highest pronoun use in informal contexts tends to be by the same people who use pronouns at high rates in formal contexts (Pennebaker and King, 1999). Analyzing function words at the paragraph, page, or broader text level completely ignores context. The ultimate difference between the current approach and more traditional linguistic strategies is that function words tell us about the psychology of the writer/speaker rather than what is explicitly being communicated. Given that function words are so difficult to control, examining the use of these words in natural language samples has provided a nonreac- tive way to explore social and personality processes. Much like other implicit measures used in experimental laboratory studies in psychology, the authors or speakers examined often are not aware of the dependent variable under investigation (Fazio and Olson, 2003). In fact, most of the language samples from word count studies come from sources in which natural language is recorded for purposes other than linguistic analysis and therefore have the advantage of being more externally valid than the majority of studies involving implicit measures. For this reason, function words are particularly useful in uncovering the relationship between intent and actual behaviors as they occur outside the laboratory.

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10 THREATENING COMMUNICATIONS AND BEHAVIOR CLASSIFICATION SCHEME FOR THREATS One of the difficulties in examining threatening communications and actual behaviors is that researchers typically do not have access to a large group of similar documents on threats and subsequent behaviors. In addi- tion, threats differ tremendously in form, type, and actual intent. Also, sit- uational features across multiple threats cannot be cleanly or confidently classified into discrete categories in order to generalize to new threats. Many of these difficulties in research on threatening communications overlap with the difficulties in research on deception, for which empiri- cal and naturalistic research has made considerable progress through the use of computerized text analyses (for a review, see Hancock et al., 2008). Comparison with Features of Research on Deception Deception has been defined as “a successful or [an] unsuccessful deliberate attempt, without forewarning, to create in another a belief . . . the communicator considers . . . untrue” (Vrij, 2000, p. 6; see also Vrij, 2008). This commonly accepted definition of deception notes several fea- tures that could be used to succinctly define threatening communications within the task of predicting behaviors (see Table 1-1). Specifically, Vrij’s definition includes information about outcome, intent, timing, social fea- tures and goals, and a psychological interpretation of the actor. Threaten- ing communications can be compared along all of these features. A threatening communication will likely carry the language cues used TABLE 1-1 Comparison of Features in Deceptive Versus Threatening Communications Features Deception Threats Outcome Successful/unsuccessful Fulfilled/unfulfilled Intent Deliberate Deliberate/not deliberate Timing Without forewarning With/without forewarning Social features/goals Create belief in another Communicate possibility of harm/no harm Psychology of actor Communicator considers Communicator considers communication to be threat to be untrue (i.e., has untrue no intent to substantiate the threat) or true (i.e., has real intent to substantiate the threat) SOURCE: Defining features of deception from Vrij (2000, 2008).

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11 USING COMPUTERIZED TEXT ANALYSIS in deception if the communicator knows that the message is false (i.e., has no intent to substantiate the threat). This situation is akin to “bluffing,” when a threat is made to achieve some goal(s) by creating in another a belief that the threat is real, when the communicator is aware that it is not. This suggests that, for text analysis of threatening communications, language cues that have reliably been found to signal deception can be used to classify this type of threat as being less likely to be fulfilled. When the communicator knows that the threat is true (i.e., has real intent to substantiate the threat), language cues that have reliably been found to signal honesty can be used to classify this type of threat as being more likely to be fulfilled. The distinction between deception and threatening communications regarding timing is also an important point. Most language samples of deception come from retrospective accounts of some event. With language samples of threatening communications, often the threatening message is revisited after the act. However, a threat, by definition, is received before the act of harm, and so the language samples analyzed to investigate threats versus deceptive messages typically come from different time points. With some threats there is the possibility of intervention. These features permit classification of four different types of threats (see Table 1-2). Threats that might have the language features of deception are bluffs and latent threats. Threats that might have the language features TABLE 1-2 Classification Scheme and Features of Threats Feature Type of Threat Real Threat Bluff Latent Threat Nonthreat Outcome Fulfilled Unfulfilled Fulfilled N.A. Intent Deliberate Deliberate Deliberate Not deliberate Timing Forewarning Forewarning No N.A. forewarning Social Communicate Communicate Communicate Communicate features/ harm harm no harm no harm goals Psychology of Communicator Communicator Communicator Communicator actor considers true considers considers considers true untrue untrue Language Honest Deceptive Deceptive Honest features of deception NOTE: N.A. = not available.

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12 THREATENING COMMUNICATIONS AND BEHAVIOR of honesty are real threats and nonthreats. Briefly described, a real threat is made known to the target before the harm occurs, with real intent to carry through on the threat. An example would be President George H. W. Bush’s threat to Saddam Hussein to leave Kuwait or a coalition attack would follow. In this case, the threat was directly communicated before- hand and was followed by the promised action. A bluff is a threat that is made known to the target but with no intent to act on the threat. Multiple examples can be found in the speeches of Saddam Hussein, who explicitly stated and implicitly suggested that his army had the capability of inflicting mass casualties on coalition forces prior to both the Persian Gulf War of 1991 and the more recent war begin- ning in 2003. Latent threats are those that are concealed to the target before the harm occurs, with real intent to carry through on the threat. An example might be the case of Bernard Madoff, who was recently impris- oned for masterminding a Ponzi scheme that bankrupted hundreds of innocent investors. Many people invested their money with Madoff under his guise of a trusted financier. In this case, no threat was communicated, but his communications with victims likely would have shown linguistic markers of deception. Nonthreats are communications from people who have no intent to harm. Indeed, nonthreats can be considered control communications in the sense that the speaker speaks honestly about events, actions, or inten- tions that the speaker believes to be nonthreatening. Nonthreats, like all other forms of threat communication, carry with them another potentially vexing dimension: the role of the listener or target of the communication. Table 1-2 is based on the speaker’s intent and behaviors, not the listener’s. It is possible that a speaker can issue a true threat but that the listener perceives it as a bluff. By the same token, latent threats and nonthreats can variously be interpreted in both benign and threatening ways. Failure to adequately detect a real threat or to falsely perceive a true nonthreat may say as much about the perceiver as the message itself. For example, Saddam Hussein’s apparent failure to appreciate coalition threats in both 1991 and 2003 very likely reflected something about his own ways of seeing the world. Just as there are likely personality dimensions of people who deny or fail to appreciate real threats, a long tradition in psychology has been interested in the opposite pattern—the belief that a real threat exists when actually one does not. Dozens of examples of this can be seen in Ameri- can politics, especially among those on the extreme left and right. During the George W. Bush years, many far-left pundits were convinced that the administration was planning to do away with the First Amendment. Currently, many right-wing voices claim that the Obama administra-

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13 USING COMPUTERIZED TEXT ANALYSIS tion wants to outlaw all guns—resulting in record sales of firearms and ammunition. From a linguistic perspective it is important that researchers explore the natural language use of both communicators and perceivers. For example, Hancock et al. (2008) have shown linguistics changes on the part of a listener who is being deceived, demonstrating that deception might be better detected and understood by considering the greater social dynamics in which it takes place (see also Burgoon and Buller, 2008, for a review of Interpersonal Deception Theory). Situations are dynamic, and there are possibilities that real threats could be revoked or unsuccessfully attempted or that bluffs might be carried out under pressure. However, the key feature in language analyses is that an attempt is made to under- stand the psychology or deep-structure processes underlying the threats. In this regard, the personality or psychological states of both speakers and targets can be assessed in order to better understand the nature, prob- ability, and evolving dynamics of a given threat. REVIEW OF EMPIRICAL RESEARCH ON COMMuNICATED INTENT AND ACTuAL BEHAVIORS uSING TEXT ANALYSIS To distinguish between real threats and bluffs, or between latent threats and nonthreats, the first step is to assess whether or not a given communication is deceptive. To detect deception, computerized text analysis methods have been applied to natural language samples in both experimental laboratory tests and a limited number of real-world settings. Typical lab studies induce people to either tell the truth or lie. Across several experiments with college students, researchers have accurately classified deceptive communications at a rate of approximately 67 per- cent (Hancock et al., 2008; Newman et al., 2003; Zhou et al., 2004). Similar rates have been found for classifying truthful and deceptive statements in similar experimental tests among prison inmates (Bond and Lee, 2005). The most consistent language dimensions in identifying truth telling have included use of first-person singular pronouns, exclusive words (e.g., but, without, except), higher use of positive emotion words, and lower rates of negative emotion words. Note that the patterns of effects vary somewhat depending on the experimental paradigm. Correlational real-world studies have found similar patterns. In an unpublished analysis by the second author and Denise Huddle of the courtroom testimony of over 40 people convicted of felonies, those who were later exonerated (approximately half of the sample) showed similar patterns of language markers of truth telling, such as much higher rates of first-person singular pronoun use. A more controversial but interesting real-world example of classifying false and true statements is in the inves-

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22 THREATENING COMMUNICATIONS AND BEHAVIOR database should include a corpus of individual-level threats—ransom notes, telephone transcripts, television interview transcripts—that can provide insight into how people threaten others. Such a database should also provide transcripts and background information on the people or groups being threatened. A third database should include natural language samples. To date, very few real-world samples of people talking actually exist. The closest are transcribed telephone calls (as part of the Linguistic Data Consortium; see, e.g., Liberman, 2009), which are recorded in controlled laboratory settings. One strategy that has promise concerns use of the Electronic Acti- vated Recorder (EAR; see, e.g., Mehl et al., 2001)—a digital recorder that can record for 30 seconds every 12 to 13 minutes for several days. Mehl and others have now amassed daily recordings of hundreds of people, all of which have been transcribed. Using technologies such as the EAR, we can ultimately get natural instances of a broad range of human interac- tions, including those that involve threats. Finally, threat-related experimental laboratory studies must be run and archived. A significant concern of the large-database approach to link- ing language with threatening communications is that it is ultimately correlational. That is, we can see how events can influence language changes. Similarly, we can determine how the language of one person may ultimately predict behaviors. The problem is that this approach is generally unable to determine if language is a causal variable. A presi- dent, for example, might use the pronoun I less often before going to war. However, the drop in I is not the reason or causal agent for the war. Curious social scientists will want to know what the drops signify. Such questions are most efficiently answered with laboratory experiments. An experimental laboratory study database could include both language samples from studies and any other data collected from the studies for further annotations. Beyond the Words: Personality, Social Relationships, and Mental Health On the surface, it might seem that the study of threatening com- munications should focus most heavily on the communications them- selves. Indeed, it is important to know about the components of written or spoken threats. However, threats are made by individuals to other individuals within particular social contexts. It is critical that any lan- guage analyses of threatening communications explore the individual differences of the threateners and the threatened within the social context of the interactions. To better assess the relationships between personality and language,

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23 USING COMPUTERIZED TEXT ANALYSIS individuals of all ages, socioeconomic status, and cultures must be sam- pled, along with any language samples that can be acquired from political leaders (e.g., Hart, 1984; Post, 2005; Suedfeld, 1994; Slatcher et al., 2007). Researchers should be encouraged to track threats that occur in every- day life. It is known from over 20 years of research studies conducted worldwide that, when asked to write about their deepest thoughts and feelings about a traumatic event, participants are often very willing to disclose vivid and personal details about highly stigmatizing traumas, such as disfigurement, the death of a loved one, incest, and rape (for a review, see Pennebaker and Chung, 2007). The ability to collect natu- ralistic evidence of long-term secrets and deception, then, is promising. Studies might come, for example, from e-mail records from individuals in the community who had kept a secret from a spouse, a lover, friends, or their boss that implied various levels of harm. Academics might also explore threats across various modalities in order to tap how a particular community or population experiences widespread threats, for example, from blogs, newspaper articles, or telephone calls (see Cohn et al., 2001, and Pennebaker and Chung, 2005). There has been much research showing that the odds of violent or approach behavior increase when mental illness is present (Dietz et al., 1991; Douglas et al., 2009; Fazel et al., 2009; James et al., 2007, 2008, 2009; Mullen et al., 2008; Warren et al., 2007). Once empirical research has reli- ably identified the linguistic features of mental illness, future research can investigate the degree to which threats are communicated by individuals with various mental illnesses and disorders. Culture and Language Threats can come from individuals and groups of varying languages and cultures. The ability to assess a threatening communication in the same language it was produced is important because there are no perfect translations. Such communications must also be assessed within the con- text of cultural experts. Below, a text analytic approach is described that the present authors are developing to assess the psychology of speakers from other cultures and determine which features are lost or gained in translations. The use of LIWC in psychological studies has extended beyond the United States, where it was originally developed. This has been made possible because the software includes a feature for the user to select an external dictionary to reference when analyzing text files. This feature, along with the ability of LIWC2007 to process Unicode text, has enabled processing of texts in many other languages. Currently, there are validated dictionaries available in Spanish (Ramirez-Esparza et al., 2007), German

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24 THREATENING COMMUNICATIONS AND BEHAVIOR (Wolf et al., 2008), Dutch, Norwegian, Italian, and Korean. Versions in Chinese, French, Hungarian, Russian, and Turkish are in various phases of development. Note that each of these dictionaries has been developed using the LIWC2001 (Pennebaker et al., 2001) or LIWC2007 default dic- tionary categorization scheme. That is, words in other languages have been forced into the English language categorization scheme used in the LIWC2001 or LIWC2007 default dictionaries. During the Arabic translation of the English default dictionary, the present authors and their colleagues also began to develop the first LIWC dictionary that was categorized entirely according to a foreign language’s grammatical scheme. Specifically, we created a LIWC dictionary accord- ing to an Arabic grammatical scheme (Hayeri et al., unpublished). Next, the dictionary was translated into English to make an English version of LIWC that would impose an Arabic categorization scheme onto English texts. Because each language affords somewhat different types of informa- tion, we should be able to see how the two languages provide different insights into the same texts. These sets of dictionaries have many potential applications in cross-cultural psychology, computational linguistics, and forensic psychology. In the case of cross-cultural psychology, demographic or psychologi- cal characteristics could be assessed in translations or in documents for which the original language is unknown. With further validation work for language style markers for psychological features such as deception or psychopathy across translations, researchers who are familiar with only the English language could conduct analyses of foreign language texts using translations into English. News articles from a given Middle Eastern region could be assessed for demographic or psychological characteristics, without full proficiency in the original language of the article. Of course, having cultural experts on hand to understand the greater social context of any communication in which it originally occurred is important, but text analyses can sometimes offer an unbiased look at a given text. In the case of computational linguistics, finding out whether, for example, language markers of sex differences are maintained in transla- tions between Arabic and English could aid in investigations of author identification for translated documents. Note that computerized word patterning methods have already been successfully applied to authorship identification and characterization for Arabic and English extremist-group Web forums (Abbasi and Chen, 2005). In the case of forensic psychology, if a translated text is presented to a researcher, it might be difficult to assess the demographic or psychological characteristics of the author if the author’s original text or language skills are unavailable or inaccessible. In another example, consider the case where some documents are available for a given subject in Arabic, while

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25 USING COMPUTERIZED TEXT ANALYSIS other documents are available only as English language translations. By using the set of Arabic LIWC dictionaries, it would be possible to assess certain features of language style in both texts and treat them as equiva- lent or be cautious in doing so in order to maximize the use of all available documents without translations. In forensic investigations this may be the case: Captured or overheard communications may be available in a given language, but more public communications might be more readily avail- able in English. Having the set of dictionaries to combine the language samples could maximize the degree to which the results are reliable and representative of communications from a particular individual or group. Clearly, more validation work is required to assess the use of the Arabic LIWC dictionaries for cross-language investigations. However, the approach laid out here can help in beginning to see the world through Arabic and English eyes using a simple word counting program for assessing language style. Although it is currently rare, some multidisci- plinary labs have come together using a rapprochement of text analytic techniques to provide a more complete psychological profile in cross- language investigations (Graesser et al., 2009; Hancock et al., 2010). Future multidisciplinary research in cross-language investigations is encouraged. Automated Classifiers: Social Language Processing The language features reported here have been shown to be predic- tors for a variety of behaviors. However, in assessing the value of the find- ings and determining how they can be applied in future investigations of actual cases of predicting behaviors, a larger framework for systematically applying language techniques to behaviors is needed. The following sec- tion describes an interdisciplinary paradigm that seeks to build an auto- mated classifier for predicting behaviors from natural language. Specifi- cally, Hancock et al.’s 2010 Social Language Processing (SLP) paradigm, which represents a rapprochement of tools, techniques, and theories from computational linguistics, communications, discourse processing, and social and personality psychology, is described. SLP has only recently been introduced by Hancock et al. (2010) as a paradigm for predicting behaviors from language. Broadly, it consists of three stages: (1) linguistic feature identification, (2) linguistic feature extraction, and (3) statistical classifier development. The first stage, lin- guistic feature identification, involves finding theoretical or empirically known grammatical or psychological features of language at the word or phrase level that might be associated with the behavior or construct in question. Hancock et al. (2010) gave the example of first-person sin- gular pronouns, or I, in the case of deception, since liars tend to divert focus from themselves in a lie. In the second stage, linguistic feature

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26 THREATENING COMMUNICATIONS AND BEHAVIOR extraction, texts whose social features are known are examined for the language feature from stage 1. In the case of deception, court transcripts (described earlier), laboratory studies (e.g., Newman et al., 2003), and political speeches (e.g., Hancock et al., unpublished) known to include deceptive and nondeceptive texts have been assessed for rates of first- person singular pronoun use, it has been shown that lies are indeed asso- ciated with a decrease in I usage (for a review, see Hancock et al., 2008). Finally, in the third stage, classifier development, a series of stages is used to classify texts according to the social construct in question and to automatically and inductively assess texts for additional language fea- tures that might improve classifier performance. Again, returning to the case of deception, the classifiers would be run on documents known to be deceptive or not, additional features that predict deception could be assessed, and then future documents of unknown verity could be assessed using the same classifier for the probability that the new document is deceptive or not. Note that there are several features of SLP that make it a suitable approach to be developed and applied for investigations of threatening communications and actual behavior. First, SLP is empirically based. That is, SLP draws from theories and case studies and from previous and continuing research on large numbers of texts. Second, SLP learns. Each of SLP’s stages can inform the others and be recursive, meaning that the classifier for each construct can be continually improved for accuracy and detection of features with additional data. For example, in the third stage, classifier development, the unsupervised machine learning techniques for inductively identifying linguistic fea- tures associated with a given construct can be especially helpful in exam- ining communications in another language for which linguistic features signaling a social or psychological construct are as yet unknown (Hancock et al., 2010). Third, SLP is probabilistic. In the prediction of behaviors, only prob- abilistic, not absolute, predictions can be made. To say that there are only a few features that predict a particular behavior and that these are completely and accurately conveyed through a set of known language features would oversimplify the complexities of human behavior and the ways in which natural language is produced. In the case of deception, human judges can barely assess deception above chance levels regardless of expertise or confidence in making such assessments (Newman et al., 2003; Vrij, 2008). Word counts have detected deception at rates that are much higher than chance (approximately 67 to 77 percent). Even these rates should not be taken too seriously. Most laboratory-based deception studies are conducted in a gamelike atmosphere, where the experiment- ers maintain tight control over the information and setting and rely on

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27 USING COMPUTERIZED TEXT ANALYSIS participants who are typically quite similar to one another and where a ground truth of 50 percent is known. The regularities found in deceptive communications even within highly curated datasets are probabilistic and with considerable error, requiring even greater caution when identifying deception in the real world. Any models of predicting behaviors must be evaluated by the rates at which they can accurately classify behaviors above chance occurrence (i.e., 50 percent) in more complicated real-world settings. Finally, SLP is deliverable. That is, the tools and techniques that SLP uses to identify and to assess linguistic features for a given construct are the same tools and techniques that would be applied to a new document. These tools and techniques are mostly free and publicly available or can be purchased online for a couple hundred dollars. The techniques require some programming skills, but these steps can be made into a Windows- based program by which most any layperson could upload a document and the computer would display a number associated with the probability that the document is either x or y (e.g., likely to lead to actual behavior or not). This is especially important for real-time or close to real-time investigations of threatening communications for which interventions are needed immediately. Note, again, that the ultimate contribution of text analyses of threatening communications will come from the degree to which text analysis informs us about the underlying psychology of the actors. CONCLuSION There has been little work so far on computerized text analysis of threatening communications. Nevertheless, several studies across sev- eral disciplines have demonstrated that word use is reliably linked to psychological states and that the underlying psychology of a speaker or author can be revealed through text analysis. With continued research on the basic relationships between natural language use and psychological states, a shared open-source or consortium-style text bank on threaten- ing communications and a multidisciplinary effort in building models to assess the probability of harm arising from threats, much progress can be made. In assessing a threatening communication it is important to under- stand the psychology of the threat’s deliverer and receiver, especially in light of the culture in which it occurs. Our responses to threats, then, can be better informed, as threatening communications dynamically unfold. With the use of text analytic tools, quick and reliable assessments and interventions may be possible.

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