Who gets called a terrorist?

This continues our series of student reflections and analysis authored by our research team.

The tPP database contains a lot of variety. There are the types of cases you would expect: bombings, shootings, attempts to join ISIS. But there are also some cases that would not necessarily spring to mind when you hear the word “terrorism”: militia activities, racially-motivated beatings, and immigration violations. For example, the database includes Francois Guagni, who illegally crossed the US-Canadian border with a knife and box cutter in his possession, as well as many others who faced similar charges as a result of counterterrorism efforts following 9/11.

Some, but not all, of the cases in the tPP database have been labelled terrorism by the US government. This labelling might occur in official reports, press releases, court documents, or during the investigation. In recent years, many have argued that there are biases in which defendants get labelled terrorists. Thinkpieces questioned why Dylann Roof and Nikolas Cruz were not widely called terrorists—was it because they were white?

I investigated the characteristics of cases which did or did not get called terrorism by the US government. From a random sample of 50 cases from the tPP database, some notable patterns arose.

For every racial category, more cases within my sample were called terroristic than not. White people made up the largest part of this sample, as well as the largest part of the cases both labelled and not labeled terroristic by the State. However, the majority of the cases that the state did not deem terroristic were prosecutions of white people. White people were much less frequently labelled terrorists by the US government than any other ethnic group.

An even starker pattern is seen in the distribution of Othered persons into the two groups. The variable “Other Status” (see Athena’s post for an in-depth explanation of this variable). Defendants are coded as non-othered when they are white or white passing, American or Western European, and Judeo-Christian. Non-white, non-western, and/or Muslim people are coded as othered. Othered persons make up the majority of the cases labelled terroristic, but only a small sliver of the cases which the US government did not call terrorism.

While these trends do not say anything causative about Roof or Cruz’s cases, it does appear that political violence by white non-Muslims is less likely to be labelled terrorism, at least in official US government speech.

– Taylor

Geography and Terrorism: Are You Likely to be Attacked?

This continues our series of student reflections and analysis authored by our research team.

Location was one of the first variables our team decided to code for within cases. Nationally and internationally, some places appear to attract more terrorist attacks than others. Why is this though? Globally, this may be easier to understand as some countries participate in significantly more violent experiences such as protest and war. The population may be made up of more identified radicals, also increasing the likelihood of violent attacks to occur. But within our own United States of America, where is terrorism more likely to happen, if anywhere?

I argue that instead of one city or state being more likely to experience a terrorist attack, it is more dependent on what is contained in that location. Different attacks are meant to serve different purposes, and the purpose of an attack is a determining factor in the geographic area it will occur. If an attacker is targeting abortionist clinics and clinicians, it is sensical to assume the offense is more likely to happen in states like California, New York, and Florida (states with the most clinics) rather than the Dakota’s, Missouri, or Wyoming (states with the least amount of clinics). Continuing, if an attack is perpetrated by an anti-military group, locations containing larger military bases are more likely to be targeted. We can predict cities who are most vulnerable, are the places containing targets that attract attacks from multiple ideologies.

An extreme example of a place that would be a terrorist attack hotspot would be a city made up of abortion clinics, military bases, government officials, chemical/energy plants, representation of all minorities, major transportation systems, etc. However, while lots of places do offer multiple of these identities, the layout of most cities allows for distribution and dispersion. So even if a town faces several attacks from different groups, they are often spread out enough that it is not always possible to determine that city as a significant hotspot. It is usually not the people or the places that are targeted, but rather conditions of the city’s intelligence that give way to targeting. If this “terrorist hotspot city” existed, we can assume extreme measures would be taken to protecting the people and places within the community.

When developing theories about where might be a more dangerous place to live, it is important to remember that terrorist attacks are only one portion of violence. Your home city being marked as a place more likely to experience an attack, maybe nearly violence-free until an attack occurs. Continuing from my previous point, the subject of your community that would designate it as a place to be targeted would also cause increased efforts toward countering any attacks. Increasing the number of government officials in one geographic location would never occur without a significant increase in security and police forces to protect them.

Our dataset, once complete, will show trends in location of attacks. A geographical breakdown could be compared to any of the other variables coded, but some that might be particularly interesting are ideology, foreign affiliation, group affiliation, lethality, and tactic. When comparing these variables, strong correlations or significant grouping in attack locations would reveal places which on average are experiencing more terrorism. And by knowing the similarities between sites, governments could work on a reorganization of layout or redistribution of counterterrorism forces.

– Tia Turner


Ettlinger, N. and Bosco, F. (2004), Thinking Through Networks and Their Spatiality: A Critique of the US (Public) War on Terrorism and its Geographic Discourse. Antipode, 36: 249-271. doi:10.1111/j.1467-8330.2004.00405.x

Global Terrorism Index 2017. PDF. New York: Institute for Economics and Peace, September 20, 2017.

Harrington, Rebecca. “The Number of Abortion Clinics in the US Has Plunged in the Last Decade – Here’s How Many Are in Each State.” Business Insider. February 10, 2017. Accessed December 05, 2018. https://www.businessinsider.com/how-many-abortion-clinics-are-in-america-each-state-2017-2.

Exploring Geographical Aspects of the tPP Dataset

This continues our series of student reflections and analysis authored by our research team.

Another dimension to researching our data involves looking at some of the geographical aspects of our data.  This is mainly through looking at the location of attacks within our dataset.  Although our data explores terrorism prosecutions within the United States, some of these attacks have taken place outside of the country.  These are especially interesting to look into as they are attacks that occurred outside of the United States, but were prosecuted within the United States.  These attacks often contain attacks on American citizens.  Concerning these attacks abroad, international law aids in determining jurisdiction over crimes occurring abroad (Kane 297).  There are five determinations of which country has jurisdiction over crimes that occur abroad.  These include: “(1) territoriality; (2) nationality of the accused; (3) nationality of the victim; (4) protection of state interests; and (5) universality of certain offences” (Kane 297).  These determinations aid in determining where crimes will be prosecuted that happen abroad.

Twenty-five of these cases involved support or association with a terrorist organization.  Nineteen involved kidnapping and these all occurred within Colombia. Another nineteen involved IEDs and military explosives.  The remaining cases involved multiple tactics.  With the country map, you can hover over each country to see the number of defendants who attacked in each specific country.  Many of the attacks that involved IEDs, explosives, and kidnappings dealt with attacks against American citizens and these defendants were therefore extradited to the United States for trial.  As for the case with support and association with a terrorist organization, most of these people were either American citizens looking to join a terrorist organization and/or someone who was caught because of an informant.


When looking at prosecutions of terrorism within the United States, one may believe that all of these attacks associated with these prosecutions also occurred within the United States.  However, through the tPP dataset, this was not found.  Of our current number of cases (1,130), 67 of these cases involved attacks that occurred outside of the United States. This is about only 6% of cases that we have coded so far, but it is an important look into some of the prosecutions of terrorism that occurs within the United States.  Through these cases, we can begin to see what type of attacks that occur outside the United States are prosecuted within the United States.  We can also begin to see why these cases are prosecuted within the United States and not in the country in which the attack occurred.  Through looking at these cases and the qualities of them, one can conclude that most of these attacks that occurred abroad were prosecuted in the United States because either the victims were American citizens or the defendants were American citizens.

– Lizzy Springer


Kane, Terry Richard. 1987. “Prosecuting International Terrorist in United States Courts: Gaining the Jurisdictional Threshold.” Yale Journal of International Law. Vol. 12, Is. 2, 294-341.

Data Validity Issues

This continues our series of student reflections and analysis authored by our research team.

The Prosecution Project is a multi-year research initiative that is run by Dr. Michael Loadenthal, of Miami University’s department of sociology, and a cohort of students. Our team has built a “code book” to help us turn the prose of court documents and news articles into consistent data values. While the code book seeks to mitigate subjective interpretations of case details, some variables — like ideological affiliation — are less black and white and require an amalgamation of different context clues from multiple sources to definitively define. As the project’s scope and team have expanded over the last two years, different variables and values have been interpreted in increasingly varying ways. That is okay, as the existing understanding of a given variable may not always be the best. Furthermore, discussions of our coding process have engaged my critical thinking skills, taught me how to articulate my research process to other teammates, and given me a clearer grasp of the Prosecution Project’s potential impact on the public and law enforcement community. However, discordance in variable interpretations — while beneficial to my overall personal growth — has made it much harder to build samples in the analysis stage of our project.

For the last month and a half, I have been analyzing our dataset in order to understand domestic terrorism motivated by anti-government belief systems. I narrowed down the 1194 cases of political violence coded to a sample of 162 cases that were ideologically aligned with anti-government extremism.

Of course, “anti-government,” to those whose research focus lays elsewhere, can be interpreted in many ways. Anti-government could be used to describe an anarchist who does not believe in the existence of a government that exercises authority without justification. Or, anti-government might be used to label a Jihadist who seeks to terrorize the American government and people by attacking a federal institution. These examples show the many ways in which “anti-government” ideological affiliation can be incorrectly but understandably assigned to defendants who clearly do not possess a rightist anti-government belief system. This is significant because any defendants incorrectly included in my sample would have skewed my data for key variables like jail sentence, ethnicity, tactic, etc.

I individually assessed each of the 26 cases in the database ideologically coded as “rightist unspecified” and “unclear” (Loadenthal et al. 2018). For each case, I read the narratives briefly describing the case and assessed the following variables: group affiliation, foreign affiliation, motivation for choosing their target (labeled in our database as “target: why”). I included defendants affiliated with right-wing, anti-government groups (e.g. Oklahoma Constitutional Militia) even if their ideological affiliation for a specific attack was unclear. I excluded defendants who possessed unspecified rightist ideologies but exclusively chose their targets based on the religion, race, or foreign nationality of the person or property targeted. Ultimately, this case by case filtering process enabled me to verify that each case included in my sample possesses the fundamental characteristics of homegrown anti-government extremism.

This blog post demonstrates one of the many ways in which our research cohort is democratically creating a communal research process, making mistakes, positively reacting to our mistakes, and reiterating and improving our database and processes. The Prosecution Project is a labor of love that is being built through a culture of collaboration, complementary skills, and continuous learning.

– Nikki Gundimeda

From the Mob to Terrorists: How RICO has Affected Prosecutions

This continues our series of student reflections and analysis authored by our research team.

The  Racketeer Influenced and Corrupt Organizations Act, commonly known as RICO, has gone far beyond the bounds of its initial intention. Enacted by congress in 1970, RICO was passed “with the declared purpose of seeking to eradicate organized crime in the United States” (Department of Justice n.d.). Under RICO, the United States Government was able to prosecute crimes committed by criminal organizations as a whole. This meant that when a case was brought against an individual, the organization that they were a part of, or in any way contributing to, would also be brought to justice. Not only can RICO be applied to a criminal act, and prosecuted at a federal level, but RICO also has civil implications. In a civil suit, RICO is a powerful weapon which allows individual citizens to bring others to court regarding criminal enterprises. However, many of the same processes still apply.

Despite having vague wording, and a broad interpretation, a RICO indictment must meet a set of criteria.The RICO act explicitly outlines 35 offenses which constitute racketeering. The most intense being “gambling, murder, kidnapping, arson, drug dealing, and bribery” (“Racketeer Influenced and Corrupt Organizations (RICO) Law” n.d.).  However, there are two offenses which are included under RICO which have the farthest reach, and allow for vast interpretation: mail and wire fraud. In order to execute a RICO indictment however, there must be proof, beyond a reasonable doubt that an “enterprise,” or criminal organization, exists. The economic reason that RICO is a federal offense, is there must be proof that the actions of the organization affected interstate commerce. Then there must be proof that the defendant was associated with the organization in question, and “engaged in a pattern of racketeering activity” (Department of Justice n.d.). And finally, that the indictment provides at least two instances of racketeering which the defendant participated in, or assisted the organization with. Both instances included in the indictment must have occurred within a ten year period. These prior offenses, known as “predicate acts” must prove the potential for future criminal activity, and/or involvement in the enterprise (Department of Justice n.d.).

The interpretation of the ‘enterprise’ is also expansive, and allows for a broad understanding by the courts, as was the initial intention of congress. Under the RICO act, “enterprise is defined as including any individual, partnership, corporation, association, or other legal entity, and any union or group of individuals associated in fact although not a legal entity” (Department of Justice n.d.). In layman’s terms, any individual, or group of individuals, de jure or de facto, which participate in acts of racketeering in order to further the goals of their organization, constitute an enterprise.

Given the inherent liberal interpretation of RICO, the US government has been able to turn the focus from organized crime such as the old Irish Mob, and begin to focus on more modern forms of organized crime. This doesn’t just mean gangs and drug cartels, however. RICO has expanded its already broad interpretation to also encompass terrorist organizations with operations in the United States. RICO has become an important part of the war on terror, despite appearing minimally in the data set thus far. Because of RICOs financial implications, prosecutors are able to apply RICO to people who attempt to provide material support to terrorist organizations. If a group is designated a terrorist group, providing material support has become a prosecutable offense. Organizations can also be prosecuted for act of terrorism “for events that occurred prior to official designations of the terrorist organizations involved,” giving RICO a more powerful role in law enforcement (Perquel 2015).

-Emily Lightman


Department of Justice. N.d. “RICO CHARGES.” U.S. Department of Justice. Accessed December 2, 2018. https://www.justice.gov/jm/criminal-resource-manual-109-rico-charges.

Perquel, Anne-Laure. 2015. “The Use of RICO in the War against Terror.” April 2015, 1-25.   http://www.academia.edu/12545128/The_use_of_RICO_in_the_war_against_terror.

“Racketeer Influenced and Corrupt Organizations (RICO) Law.” n.d. Justicia Law. Accessed December 2, 2018. https://www.justia.com/criminal/docs/rico/.

Group Affiliation and Ideologies Through Heat Maps

This continues our series of student reflections and analysis authored by our research team.

When tPP was still in its coding stage, I enjoyed perusing through documents to find the codable variables within our dataset. I was consistently interested in where defendants fell in terms of ideology and group affiliation. I would often notice similarities between certain groups. I had questions about what may have an influence on the patterns I was seeing. When deciding on a topic for my analysis, I knew that I wanted to look into group affiliation on a deeper level.

A defendant’s affiliation generally has a big impact on a multitude of other variables like othered status, location of attack, foreign affiliation and more. The challenging part of focusing on group affiliation was deciding on what other variables to investigate in combination with it. My first attempt at an analysis of the dataset looked at the effect of othered status and foreign affiliation on group affiliation versus no affiliation. I was able to use the numbers from the dataset to produce a large amount of facts and figures, but it quickly proved to be too many variables and far too many patterns to analyze. I did not find relevant patterns in the areas I was expecting, either. I was interested in far too many variables to produce an intuitive paper that explored group affiliation in the manner it deserves.

I started to organize the entire dataset by utilizing pivot tables within Excel. Pivot tables are tables of statistics used to summarize another, larger data table such as tPP’s. With this feature, I was able to easily insert or remove any variable from the dataset in order to see what kind of findings it would produce. I stuck with using group affiliation as a base and Excel provided me with massive tables, grouping defendants into rows. It showed me precisely how many of them were involved with each group. When inserting a variable like foreign affiliation, I was able to easily see which defendants had a connection to a foreign country. The titles shown on the table were no, yes and unknown which are the three options for foreign affiliation coding. Underneath each title was a list of group affiliations that fit into the category. Each group affiliation row showed the number of defendants that applied to one of the categories. While using each of the 1,194 defendants data produced an overwhelming amount of numbers, I was able to identify a handful of clear and relevant patterns. For example, 91.6% of the defendants who are affiliated with Al Qaeda are foreign affiliated (132 people). When looking at the 348 defendants who have no group affiliation, the PivotTable clearly shows an overwhelming majority (330 of 348) have no foreign affiliation. This method of organizing the data’s numbers was very helpful in deciding what specific groups and variables would produce information worth investigating further.


The sample of tables below show a part of a pivot table that looks at group affiliation in relation to foreign affiliation as described above.

While I enjoyed looking into each of the variables, I struggled to write an analysis on group affiliation in relation to just one of the other variables. I ended up using a lot of the relevant findings in my writing, but it did not come together well; it was too broad. I eventually decided to analyze the data in a way that I had not considered before. I chose to use heat mapping to represent where individual attacks had occurred. Rather than putting all of the data into the system, I chose to sort by group affiliation. Through this method, I have begun to reveal findings through analyzing group affiliation and ideology in relation to geographic location which has already proven to show relevant patterns of attack.

– Jessica Enhelder

Reexamining the Codebook

This continues our series of student reflections and analysis authored by our research team.

In class the past few weeks, we have decided to reassess and rework our team’s Codebook. Up until now, the Codebook has been a relatively straightforward manual that we all follow while coding each case. Personally, my partner and I leave the Codebook open in another tab while we research a defendant – referring to it when we have specific questions about what codes make up a certain variable or how exactly a variable is defined.

For the most part, we have the Codebook memorized. Since the majority of us joined the team as the Codebook was being created and fine-tuned, we understand what each variable is looking to assess. This is not to say the Project has not come across some interpretation issues as we have worked through coding. A few weeks ago, we had a lengthy discussion about how to code cases in which the defendant was a minor at the time of the crime. Since portions of these cases, including ages of the defendant are often sealed from the public, it can be difficult to know what the actual age was. Some members of the team had been coding this variable as unknown, while others had been using 17 as a fill-in number, as our Codebook had not specified what to do in this situation. We talked in-class and decided to use 17 as our age for all minors, unless the actual age was known, in which case we would use that one.

Differing interpretations of the Codebook occur regularly, and we do our best to address them and amend the document to make each variable description even more clear. However, next semester, we are adding several new members to the Prosecution Project. These new additions will have the guidance of senior members of the team as they learn how to code, but they will still be heavily reliant on the exact wording of the Codebook to understand each variable and its codes. Because of this, the team has spent the last two classes going through each individual variable in the Codebook in extreme detail. Our goal is to phrase the Codebook so that there can be almost no room for misinterpretation. We begin by phrasing each variable as a question.

This sounds relatively straightforward and self-explanatory, but as the team and I can assure you, it is not. We have dedicated at least ten minutes to discussing how to phrase the questions for each variable, and some variables have required conversations lasting over an hour. One particularly challenging and divisive conversation occurred surrounding the variable of “previous similar crime”. We struggled with how to conceptualize what constituted a similar crime, and by the end of the discussion, we had probably run through thirty different variations of the question. Our first suggestion was, “Has the defendant been charged with a previous similar crime?” and by the end, we decided on, “Has the defendant been charged or convicted of a previous crime motivated by the same belief system?” We felt that this phrasing was the best way to encompass what we wanted to assess with this variable.

Eventually, we will have worked through this process for every variable in our Codebook. Ideally, we will have a user-friendly manual for next semester’s additions, but we are ready and willing to further adjust our Codebook as new issues arise.

– Zoe Belford

A Codebook 2.0?

This continues our series of student reflections and analysis authored by our research team.

 As the semester is winding down, here is an update on the current status and goals of tPP! Over the past two months, everyone has worked to construct mini-analyses papers on a chosen topic surrounding our database. Some members worked in pairs while others worked individually to assess trends that may be appearing within the database. The papers addressed several different factors we have coded for such as gender prevalence in terrorism, foreign affiliation and fatality, military/veteran status and its role in attacks, location prevalence, etc. We plan to start out next semester by presenting our papers and findings to the entire team as a reminder of all the great work we have achieved so far!

Speaking of the team, we are also excited next semester to welcome some new recruits! We have spent time recently reviewing our meeting agenda and drafting not a new, but a more explicit, and more concise codebook that will be extremely beneficial when catching the new members up to speed! Kicking off the new year, we will ultimately finish adding and coding cases, so we can continue to draw final analyses of patterns of taxonomy within our dataset. As we begin to move toward the final stages of our project, we aim to draft more literature, advertising the information presented in our data, and work to present our findings to outsiders at relative conferences!

As a member of tPP from the start, and a soon graduating senior, this experience has been eye-opening as much as it has been informative. Working with Dr. Loadenthal and the rest of the team has caused an interest in continuing research around the justice system and helped prepare me to keep to higher education in ways I would have never received without them. With tPP being one of the largest datasets of its kind, it has offered so many undergraduate students the chance to participate in research that while tasking, has been extremely rewarding. The project is mostly student-led has allowed us to learn and improve our skills in leadership, collaboration, research, statistical analyses, technical writing, and so many more. Many members have been on the team since the start and found their niche through this project and enjoy the chance to collaborate on a regular basis to adopt roles and goals as needed within our own mini projects and the larger project as a whole. This spring will be an exciting time for everyone as we move to our final stages but the time cannot come soon enough for our eager current and new members.

– Tia Turner

tPP in the news again! This time a short video interview with project Director

Following coverage of tPP by our university news, tPP Director Dr. Michael Loadenthal sat down with Sinclair Broadcast Group for a 30-minute interview about the project, the state of political violence in the US, and the challenges of researching these matters. From this interview we are happy to share a short segment produced by Sinclair below.

We were also happy to be mentioned in Miami University’s College of Arts and Science Alumni Update for November 2018 which you can see below:

Excluded Cases and Why They Remain Important

This continues our series of student reflections and analysis authored by our research team.

The tPP data set[1] has an extensive process of selecting cases that fit the criteria for the database.  This process is called the decision tree and has been described in other blog posts.  While the data set currently has almost 1,202 coded cases, there are some cases that did not meet the qualifications of the decision tree at some point in time and ended up becoming excluded.  These cases appear to be ones that would be relevant to the set but they fall short of particular qualifications.  When a case is excluded it is placed into a document of excluded cases where it is briefly explained and then its exclusion is subsequently explained as well.  Some may wonder why we bother to record cases that are not matches for us, well, many these excluded cases can reveal information about the tPP data set itself.

Some excluded cases are straightforward to explain, such as the case of William Rodgers.[2]  William Rodgers was an environmental activist and major leader of an act of arson at a Vail Ski Resort in Colorado.  He is excluded from the data set because he committed suicide in jail shortly after he was arrested.  Since he was not able to be charged and prosecuted, he is excluded from the tPP data set.

Other cases in the excluded cases file deal with more complicated issues such as intent.  Intent becomes crucial in determining whether or not to omit particular cases from the dataset.  Does the individual committing the act of terrorism or political violence truly possess a political motive?  Are their crimes attempting to further a particular terrorist organization or movement?

The tPP dataset contains many variables that are coded with very precise language to ensure that intent is the primary focus of the coding.  Some of these variables include ‘people versus property’ or ‘ideological affiliation’.  People versus property outright asks “Did this crime intend to target human beings, material property, both or neither?”[3]  This seeks to determine the intent of whom the crime was trying to cause harm towards.  Ideological affiliation is defined by the codebook as “What belief system, if any, motivated the defendant to commit the crime?”[4] This variable also focuses on what the core value system of the individual is and this can affect the intention of their crime.  If one throws a brick threw a McDonald’s window out of anger it would not be considered terrorism.  However, if they had an ideology that opposed consumption of animals and they committed the same crime, the same act could be considered an act of political violence, and likely termed by the government as ‘eco-terrorism’.

These variables show the emphasis that the data set places on intent.  The excluded cases are a variety of examples where the acts may be heinous, or may present rhetoric that is similar to what one may consider to be terrorism, however, this specific data set takes into careful consideration intent, and every case must fully pass through the decision tree before it qualifies to be coded into the data set.  These excluded cases are still valuable, as they show the value this tPP data set places on intent.

– Hannah Hendricks


[1]Loadenthal, Michael, Zoe Belford, Izzy Bielamowicz, Jacob Bishop, Athena Chapekis, Morgan Demboski, Bridget Dickens, Lauren Donahoe, Alexandria Doty, Megan Drown, Jessica Enhelder, Angela Famera, Kayla Groneck, Nikki Gundimeda, Hannah Hendricks, Isabella Jackson, Taylor Maddox, Sarah Moore, Katie Reilly, Elizabeth Springer, Michael Thompson, Tia Turner, Brenda Uriona, Brendan Newman, Jenn Peters, Rachel Faraci, Maggie McCutcheon, and Megan Zimmerer, 2018. “The Prosecution Project (tPP) October 2018” Miami University Sociology Department. https://tpp.lib.miamioh.edu. Loadenthal 2018. “The Prosecution Project (Decision Tree)”

[2] (Loadenthal et. al, 2018)

[3] (Loadenthal et. al, 2018)

[4] (Loadenthal et. al, 2018)