Public support for counterterrorism efforts using probabilistic computing technologies to decipher terrorist communication on the internet

Abstract

Advancements in big data analytics offer new avenues for the analysis and deciphering of suspicious activities on the internet. One promising new technology to increase the identification of terrorism threats is based on probabilistic computing. The technology promises to provide more efficient problem solutions in encryption and cybersecurity. Probabilistic computing technologies use large amounts of data, though, which raises potential privacy concerns. A study ( N = 1,023) was conducted to survey public support for using probabilistic computing technologies to increase counterterrorism efforts. Overall, strong support was found for the use of publicly available personal information (e.g., personal websites). Regarding private personal information (e.g., online conversations), respondents perceived it to be more appropriate to use information from out-group members (non-American citizens) than from in-group members (American citizens). In line with a social-identity account, this form of in-group favoritism was strongest among respondents displaying a combination of strong national identities and strong privacy concerns.

When Those With Privacy Concerns Show Stronger In-Group Favoritism: Using Personal Information From In-Group and Out-Group Members to Identify Terrorist Threats

“…Specifically, we tested four hypotheses derived from a social-identity framework (Tajfel & Turner, 1986) that describe the independent and combined effects of four factors on the acceptance of the use of personal information to prevent terrorist attacks: whether information is being gathered from members who belong to one’s own group (the in-group) or an out-group; whether the collected information is private or publicly available; and respondents’ national identity and overall sense of privacy regarding online information. The proposed hypotheses were supported in a recent study (see Reimer & Johnson, 2022) that used an MTurk (Amazon Mechanical Turk) sample of N = 1,023 participants and provided participants with a newspaper article describing a specific attack that was developed by Hinsz and Betts (2014). We aimed to extend on the study by Reimer and Johnson (2022) in the following ways: (1) test the hypotheses with a new sample of participants ( N = 1,204); (2) use a variety of different possible terrorist attacks that have been described in the literature (see Liu et al, 2019); (3) focus on the observed in-group favoritism regarding the surveillance of private information and compare the effect more closely to other effects of in-group favoritism.…”

“…In 2016, the U.S. government took Apple to the Federal court to try and force Apple to allow them into the private phone of an attacker in the mass at San Bernardino, California (Perlroth, 2019). Apple refused to fulfill the request, being concerned about this concession opening up a door of opportunity for the government to begin more and more invasive surveillance.…”

“…Apple refused to fulfill the request, being concerned about this concession opening up a door of opportunity for the government to begin more and more invasive surveillance. The York Times (Perlroth, 2019) reports this long-lasting power struggle as an ongoing battle between big tech companies and the U.S. government. This raises an important question for the prevention of terrorist attacks.…”

“…This proposed favoritism is not necessarily based on negative views of an out-group (Allport, 1954; Brewer, 1999), acting independently of, though not precluding, any negative biases. Hinsz and Betts’s (2014) hypothesis was not supported in two studies with undergraduate students and did not find support in a follow-up study with an MTurk sample (see Reimer & Johnson, 2022). In all three studies, similar support was observed for counter-terrorism efforts for both in-groups and out-groups.…”

“…In-group favoritism has also been demonstrated in the context of terrorism and anti-terrorism (e.g., Levy & Rozmann, 2022) and has been linked to institutional out-group discrimination and support for anti-immigration policies (Doosje et al, 2009). Like Reimer and Johnson (2022), we started out to explore another possible form of in-group favoritism that is concerned with the use of personal information that raises potential privacy concerns. The literature on social identity suggests that individuals trust in-group members more than out-group members (Cruwys et al, 2020), and they are less concerned sharing information with in-group than with out-group members (Bingley et al, 2022).…”