Short Essay Questions Flashcards
How would terrorist networks be different from other networks?
COVERTNESS: Terrorist networks differ from other networks in several key ways, primarily due to their covert nature and the goals they pursue. Terrorist organizations often operate in secrecy to avoid detection by law enforcement and intelligence agencies. Their activities, recruitment, and communication are conducted discreetly, often using encrypted or hidden channels. The structure of these networks can be hierarchical or decentralized, but the emphasis is on maintaining operational security.
IDEOLOGY: Terrorist networks also have a more distinct focus on ideological or political goals, often driven by extremist beliefs. This ideological foundation binds members together, making loyalty and secrecy vital.
TRUST BASED: Additionally, these networks tend to rely on trust-based relationships, where members often have limited interactions outside the network to protect their identities and reduce the risk of infiltration.
Milla et al. (2020) show how operational
leaders have high betweenness centrality and
ideological leaders do not. They conclude they are more important.
What do you think this might mean?
Is this a reasonable conclusion?
Milla et al. (2020) suggest that operational leaders have high betweenness centrality because they act as intermediaries between different parts of the terrorist network, facilitating communication and coordination. This makes them crucial for the network’s operational effectiveness, as they connect various subgroups, ensuring smooth execution of attacks and activities. In contrast, ideological leaders, despite their influence on recruitment and cohesion, may not occupy the same structural position within the network, which is why their betweenness centrality is lower.
This conclusion is reasonable in the context of the operational function of terrorist networks, where coordination and planning are central to success. However, it could overlook the importance of ideological leaders in shaping long-term strategic goals, maintaining group unity, and driving radicalization. While operational leaders may be more pivotal in day-to-day operations, ideological leaders could be essential for the network’s sustainability and broader vision, making both roles integral in different ways.
How might terrorist groups develop their
network to increase their survival?
What kind of ties should they develop?
And what kind of ties should they avoid?
Terrorist groups aiming to increase their survival would strategically develop both strong and weak ties within their network. Strong ties, such as close relationships between key operatives and leaders, ensure reliable communication, trust, and coordination for planning and executing attacks. These strong internal ties help maintain group cohesion, security, and loyalty. Additionally, building weak ties with other groups or external actors, such as financiers or suppliers, can increase their resource access without revealing too much about their inner workings. These weak ties expand their influence, access to resources, and potential for external support.
However, terrorist groups should avoid ties that could compromise their security or stability. Over-reliance on centralized leadership or tight-knit structures can create vulnerabilities, making the group more susceptible to detection and disruption. Likewise, overt ties to state actors or organizations with conflicting goals could lead to betrayal or unwanted exposure. Thus, a balanced network that ensures both operational efficiency and covert external alliances is crucial for survival.
For clandestine networks it is often difficult to get a full picture of the network. For each of the following measures, how badly do you think they are affected by missing data?
1 Degree centrality
2 Betweenness centrality
3 Community detection
- Degree centrality: Degree centrality, which measures the number of direct connections an actor has, is relatively robust to missing data. While missing connections can affect accuracy, degree centrality will still provide useful insights if the missing data involves only a small portion of the network. However, if large portions of the network are missing, degree centrality may underestimate the influence of central actors.
- Betweenness centrality: Betweenness centrality, which identifies individuals that act as bridges between different parts of the network, is highly sensitive to missing data. If key connections or parts of the network are missing, it could distort the flow of information and underestimate or misrepresent the importance of certain individuals, and also affect other measures like eigenvector. The loss of data can significantly alter the perceived connectivity of the network.
- Community detection: Community detection is heavily impacted by missing data, as the identification of groups within a network depends on accurate relationships between nodes. Missing connections can lead to misidentifying communities or incorrectly associating nodes with other groups. Therefore, community detection is highly vulnerable to incomplete network data.
Hofmann (2020) identifies four different
types of networks, ”full”, ”ideology”,
”signaling” and ”support”.
What are the ties and nodes in each?
What do you think of this approach?
- Ideological Networks: The nodes in this network are individuals or groups sharing extremist beliefs, either online or offline. The ties are the shared ideological beliefs that connect these individuals, often forming the basis for radicalization.
- Signaling Networks: The nodes are lone-actors and other individuals seeking validation. Ties in this network are the communication channels, often through online platforms, where the lone-actor expresses intent or seeks recognition from others before carrying out an attack.
- Support Networks: The nodes in this network include close associates or online groups that provide emotional or logistical assistance. The ties are the relationships that offer crucial support, from encouragement to material help.
- Full Networks: This encompasses all the above ties combined, representing the broader network of connections that influence the lone-actor.
This approach is valuable because it challenges the isolation myth of lone-actors, emphasizing the importance of understanding their broader network ties for more effective counter-terrorism strategies. It encourages a more comprehensive and nuanced view of lone-actor terrorism.
Siegel (2011) assumes that internal
motivation to protest is unrelated to the
position in the network.
Is this a reasonable assumption?
How might it not be?
Siegel’s (2011) assumption that internal motivation to protest is unrelated to someone’s position in the network may not be entirely accurate. In reality, where someone is located in the network could influence their motivation to protest. For example, people in central positions, who are more connected to others, might feel more responsible to act, as they may believe their involvement is important for the group. On the other hand, those on the edges of the network might feel less motivated, as their actions may seem less impactful.
Additionally, individuals who are deeply connected to ideological or support networks might be more motivated to protest due to shared beliefs or the support of others. This suggests that internal motivations to protest are influenced by both personal beliefs and the person’s role within the network, meaning the network’s structure can affect not just how protests spread but also why people decide to participate.
We have now read about theoretical models (Siegel, 2009, 2011), social media
(Steinert-Threlkeld, 2017; Larson et al.,
2019; Chen, Oh and Chen, 2021; Masterson, 2024) and general channels (Andrews and Biggs, 2006) of diffusion.
How do these align?
How would the role of media in Andrews
and Biggs (2006) impact on the cascades
in Siegel (2009, 2011)?
The readings explore how networks and media influence collective action and social movements. Siegel (2009, 2011) looks at how the structure of networks—like who is connected to whom—affects whether people decide to join protests. He finds that central figures in a network can encourage others to join, especially when more people start participating. Similarly, Chen, Oh, and Chen (2021) focus on how online media helps spread information quickly and connect people for collective action by reducing barriers and providing emotional motivation.
Andrews and Biggs (2006) discuss the role of media in spreading ideas and helping people connect. In Siegel’s work, media could make it easier for people to join collective actions by sharing information and reducing the perceived risk of participating. Together, these studies show how networks, media, and social connections can work together to encourage or discourage people from participating in large-scale collective actions.
Siegel (2011) models repression as the
removal of nodes from the network and
investigates when protest cascades are
stopped. Based on theories we discussed:
What nodes do you think are particularly
important to keep?
What features of a network do you think
make it more or less robust?
In Siegel’s (2011) model, nodes represent individuals in a protest network, and repression involves removing these nodes to disrupt the movement. Key nodes to keep are those with high centrality, such as influential leaders or individuals with many connections. These nodes are important because they help spread information and motivate others to join the protest. If these nodes are removed, the protest is likely to lose momentum.
A network’s robustness depends on its structure. A tightly-knit network with many strong connections between individuals is less robust, as removing a few central nodes can cause it to collapse. In contrast, a more decentralized network with weak ties between individuals is more robust. Even if some nodes are removed, the network can still function, and other individuals can fill the gap. Therefore, decentralized networks are more resilient to repression and can maintain collective action despite efforts to break them down.
Steinert-Threlkeld (2017) argues that when the periphery of the network tweets about a protest, it is much more likely to mobilize than when the core does.
Why might this be the case?
Steinert-Threlkeld (2017) suggests that when individuals on the periphery of a network tweet about a protest, it is more likely to mobilize because they often reach new, less engaged audiences. These peripheral individuals are not as tied to the core group, so their messages can spread further, attracting people who might not otherwise be involved. Tweets from the core, however, are likely to reach a smaller, already mobilized audience, leading to less new participation.
Additionally, messages from peripheral nodes can appear more authentic or relatable to others outside the core group, creating a sense of inclusivity and broadening support. In contrast, messages from the core may be seen as part of a pre-existing movement, which could reduce their effectiveness in mobilizing new participants. Thus, peripheral actors can play a crucial role in expanding the reach of a protest and encouraging broader participation.
Andrews and Biggs (2006) use membership of the same athletic organisation as a proxy for social networks and distribution of local newspapers as a proxy for media effects.
If you were to study a contemporary protest:
How would you approach this?
What would be good proxies for media
channels, social networks, etc.?
To study a contemporary protest, I would approach it by identifying key factors that influence mobilization, such as social networks and media channels.
For social networks, I could look at participants’ affiliations with specific online communities (e.g., social media groups, online forums) or real-world networks (e.g., university or workplace groups). Platforms like Facebook, Twitter, or WhatsApp could be proxies for social connections, as they enable the rapid spread of information.
For media channels, I would analyze the role of digital media platforms, including Twitter, Instagram, and YouTube, where protest organizers often share information and call to action. Traditional media like newspapers and TV might still be relevant, but social media plays a bigger role today in spreading information quickly, especially among younger, more tech-savvy individuals.
I would track both social media interactions (hashtags, mentions) and the spread of protest-related news to understand the interaction between online networks and media coverage in mobilizing participants.
How does the k-cores measure differ from other centrality measures?
The k-core measure in network analysis identifies the most cohesive groups within a network, focusing on nodes that are part of a core subgroup with a minimum number of connections (k). It finds subsets of nodes where each node is connected to at least k other nodes in the group. This differs from centrality measures, which focus on individual nodes’ importance within the entire network.
For example, degree centrality measures how many connections a node has, betweenness centrality tracks the number of times a node acts as a bridge along the shortest paths between other nodes, and closeness centrality gauges how quickly a node can reach others. In contrast, k-core looks at the network’s structure in terms of subgroups rather than individual node influence. While centrality measures highlight individual prominence, k-core highlights the robustness and resilience of tightly connected subgroups, emphasizing overall network cohesion rather than specific nodes.
In both Siegel (2009) and Chen, Oh and
Chen (2021), being in a highly connected
position does not necessarily imply that you are more likely to participate in collective action.
What is the reason for this?
In both Siegel (2009) and Chen, Oh, and Chen (2021), being in a highly connected position does not always increase the likelihood of participating in collective action because the type of connections and the flow of information matter more than mere connectivity.
In Siegel (2009), individuals in central positions may not participate if they perceive high personal risks or lack sufficient incentives, even if they are well-connected. Central positions may offer influence but don’t automatically translate to action.
Similarly, in Chen, Oh, and Chen (2021), being in a highly connected position within an online network doesn’t guarantee action because individuals need motivation, emotional appeal, and the right kind of information to participate. Even with high connectivity, individuals might not engage if they don’t feel personally compelled or if the collective action does not align with their interests. Therefore, the quality and type of connections, as well as personal motivations, influence participation more than simple network centrality.
Masterson (2024) finds that densily
connected groups are more likely to engage in dialogue, but lack information on resources available to them, compared to less densily connected groups.
What do you think explains this?
How does this relate to other literature we have read in the module so far?
Masterson (2024) suggests that densely connected groups are more likely to engage in dialogue because strong bonds within the group encourage communication and trust. However, these groups may lack information on external resources because their connections are more focused on internal support and less on linking to outside networks or broader sources of information.
This ties into the concept of “bonding ties” versus “bridging ties” in social network theory. Bonding ties strengthen internal relationships but limit access to new information, while bridging ties connect groups to external resources. In the module, we’ve seen that tightly-knit networks can be powerful for cooperation and emotional support, as seen in Siegel (2009), but they might not provide the information needed for mobilization or resource access, as discussed in Masterson (2024). So, while strong internal connections foster solidarity, weak external ties limit broader awareness and opportunities.
What are the nodes and the ties in the
refugee networks of Masterson (2024)?
The nodes represent the individuals or groups within the refugee community, as well as external actors like host communities or NGOs.
The ties are the connections between these nodes. There are two types of ties:
- Bonding ties: These are connections within the refugee community, providing informal support such as resource-sharing, emotional support, and mutual aid. These ties are strong but may limit access to external resources.
- Bridging ties: These are connections between refugees and external actors, such as host communities or NGOs. These ties are crucial for accessing formal resources like government assistance, job opportunities, and broader networks.
Masterson emphasizes the importance of balancing both types of ties for refugees’ integration and resource access, as bonding ties support immediate needs, while bridging ties connect refugees to broader resources that are essential for long-term survival and cooperation.
Larson et al. (2019) compare the network of Twitter users with tag #JeSuisCharlie who were geotagged at the location of the protest, with others with the same tag who were geotagged elsewhere in Paris at the time.
What is the motivation for this comparison?
How do they construct the remainder of the network?
The motivation for Larson et al. (2019) to compare the network of Twitter users with the hashtag #JeSuisCharlie who were geotagged at the protest location versus those geotagged elsewhere in Paris is to investigate how proximity to the event and social network connections influence protest participation. They aim to understand whether being physically closer to a protest leads to greater mobilization, and how social ties shape participation.
To construct the remainder of the network, they analyze the relationships between Twitter users based on their interactions, such as retweets, mentions, and shared hashtags. They focus on the strength and political activity of users’ connections, examining how exposure to protest-related content spreads within networks. By analyzing these online connections, the study shows how individuals within tightly connected, politically active networks are more likely to participate in protests due to peer influence and information diffusion.