Working papers

1. As Generative Models Improve, We Must Adapt Our Prompts

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   In an online experiment with N = 1893 participants, we collected and analyzed over 18,000 prompts and 300,000 images to explore whether prompting needs to adapt as generative AI models evolve. Participants were randomly assigned to use DALL-E 2 or its advanced successor DALL-E 3. Each participant wrote prompts to reproduce a target image as closely as possible across 10 consecutive attempts. We find that participants using DALL-E 3 outperformed those using DALL-E 2, with the performance gap driven equally by: (1) the increased technical capabilities of DALL-E 3, and (2) endogenous changes in participants’ prompting in response to these increased capabilities. Furthermore, participants across all skill levels learned to adapt their prompts to new models quickly, with minimal training. These findings suggest that as AI models continue to improve, firms will need to maintain and update their technical infrastructure and prompts. However, these adjustments will not be prohibitively complex, reducing the need for costly prompt engineering expertise. The implications for businesses are substantial, as the ability to easily adapt prompts may democratize access to AI’s benefits, mitigating concerns about inequality in the AI-driven economy.

2. Network interventions to increase social cohesion

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   As social media platforms become an increasingly inseparable part of our social structure, there are growing concerns about the prevalence of hate content on these platforms. Several social media companies have embarked on content moderation, through activities such as content removal or user bans, as a way to combat online hate speech. Existing evidence as to whether these punitive measures are effective at reducing hate speech are mixed. We describe a network-based experiment designed to measure the effectiveness of empathy-based counterspeech and celebrity messaging in curbing hateful language within the entire Nigerian social media landscape. The experiment design follows graph cluster randomization which accounts for interference between units. The design notably allows us to identify both the spillover and direct treatment effects with Horvitz-Thompson and Hajek estimators. The star-like structure and degree distribution in the network pose considerable challenges to achieving precise estimations. Subsequently we discuss several techniques that we implemented to reduce the variance of ATE, including a weighted exposure model, difference estimation and construction of more cohesive clusters.

3. Network Structure of Unequal Diffusion

   Eaman Jahani, Dean Eckles, and Pentland, Alex

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   We develop a theoretical network model that captures the network structure of unequal diffusion. First, we argue that homophily alone does not fully explain the extent of unequal diffusion in the network. We show that any departure from the uniform distribution of links to information sources among members of a group limits the diffusion of information to the group as a whole. The variation from equal opportunity baseline not only has first order effects since some individuals will have fewer direct links to information sources, but also second order effects as the whole group will have fewer diffusion paths to the information sources. Second, we show empirically that most networks exhibit higher susceptibility to unequal diffusion across various grouping variables (e.g. gender or race) than expected simply by measures of homophily. Finally, we develop a variant of the Stochastic Block Model that parameterizes the cross-group linking of each node and by doing so fits not only assortativity on paths of length 1, but also diffusion assortativity on paths of length 2 and 3.

4. Tie Strength and Length in Social Capital: Evidence from Charitable Fundraising

   Eaman Jahani, Michael Bailey, and Eckles, Dean

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   We contribute to the literature on network structure and performance by studying the local network structure around individuals with the best outcomes. We propose the concept of bandwidth-diversity matching, according to which individuals who match the tie strength to their contacts with their structural novelty achieve truly diverse networks and obtain better outcomes. We investigate this in the context of birthday fundraisers on Facebook. We show that users whose networks is composed of a mixture of short embedded ties and long ties are more likely to reach their fundraiser goal than those with one type exclusively. More importantly, the stronger the long ties are, the more successful the campaign will be. In other words, the most successful fundraisers occur when the ego has cultivated a mix of embedded ties and strong-long ties.

5. Rivalrous Resource Sharing in Networks can Exacerbate Existing Inequalities

   Eaman Jahani, Dean Eckles

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   We examine the network mechanisms that widen inter-group differences and lead to different levels of social capital in different groups. We study an information sharing game, in which individuals have to compete for a rivalrous resource over repeated rounds. The equilibrium predicts lower cooperation among lower status agents, which leads the whole group to receive a small share of the common resource. We further validate this prediction in an online lab experiment by recruiting individuals to play an online collaborative game in which they have to find and dig gold mines and in the process can pass information to their neighbors according to the network structure. The players which had a lower chance of receiving the gold were less likely to share it with their contacts. As a result, these low status players took “a smaller share of the pie” than what was expected by their individual differences.

Refereed

1. Long ties, disruptive life events, and economic prosperity

   Jahani, Eaman, Fraiberger, Samuel P., Bailey, Michael, and Eckles, Dean

   Proceedings of the National Academy of Sciences 2023

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   Social networks shape and reflect economic life. Prior studies have identified long ties, which connect people who lack mutual contacts, as a correlate of individuals’ success within firms and places’ economic prosperity. However, we lack population-scale evidence of the individual-level link between long ties and economic prosperity, and why some people have more long ties remains obscure. Here, using a social network constructed from interactions on Facebook, we establish a robust association between long ties and economic outcomes and study disruptive life events hypothesized to cause formation of long ties. Consistent with prior aggregated results, administrative units with a higher fraction of long ties tend to have higher-income and economic mobility. Individuals with more long ties live in higher-income places and have higher values of proxies for economic prosperity (e.g., using more Internet-connected devices and making more donations). Furthermore, having stronger long ties (i.e., with higher intensity of interaction) is associated with better outcomes, consistent with an advantage from the structural diversity constituted by long ties, rather than them being weak ties per se. We then study the role of disruptive life events in the formation of long ties. Individuals who have migrated between US states, have transferred between high schools, or have attended college out-of-state have a higher fraction of long ties among their contacts many years after the event. Overall, these results suggest that long ties are robustly associated with economic prosperity and highlight roles for important life experiences in developing and maintaining long ties.

2. Implications of COVID-19 vaccination heterogeneity in mobility networks

   Communications Physics 2023

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   Our study utilizes network science to examine how uneven vaccine distribution affects mass vaccination strategies in the United States. Using mobility network data and epidemiological models, we find that distributing a fixed quantity of additional vaccines across Census Block Groups (CBGs) can vary case count reductions by up to 200l%. This highlights the impact of vaccination heterogeneity in mobility networks on epidemic outcomes. Our efficient algorithm identifies optimal vaccine distribution for maximum case reduction. Simulations show a possible 9.5% decrease in case numbers with just a 1% increase in the national vaccination rate if vaccines are optimally distributed. This result surpasses those from other vaccine distribution models. Our findings underline the need for policymakers to understand the interaction between vaccination patterns and mobility networks, suggesting that grasping geographical vaccine uptake variations could be as crucial as raising the overall vaccination rate.

3. An Online experiment during the 2020 US–Iran crisis shows that exposure to common enemies can increase political polarization

   Jahani, Eaman*, Gallagher, Natalie M*, Merhout, Friedolin*, Cavalli, Nicolo*, Guilbeault, Douglas*, Leng, Yan*, and Bail, Christopher A*

   Scientific Reports Jul 2022

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   Longstanding theory indicates a common enemy can mitigate conflict between members of rival groups. We tested this hypothesis in a pre-registered experiment where 1,670 Republicans and Democrats in the United States were asked to complete a collaborative online task with an automated agent or “bot" that was labelled as a member of the opposing party. Prior to this task, we exposed respondents to primes about a) a common enemy (involving threats from Iran, China, and Russia); b) a patriotic event; or c) a neutral, apolitical prime. Though we observed no significant differences in the behavior of Democrats as a result of these primes, we found that Republicans—and particularly those with very strong conservative views—were significantly less likely to cooperate with Democrats when primed about a common enemy. We also observed lower rates of cooperation among Republicans who participated in our study during the 2020 Iran crisis, which occurred in the middle of our data collection. These findings indicate common enemies may not reduce inter-group conflict in highly polarized societies, and contribute to a growing number of studies that find evidence of asymmetric political polarization. We conclude by discussing the implications of these findings for research in social psychology, political conflict, and the rapidly expanding field of computational social science.

4. Social Debunking of Misinformation on WhatsApp: The Case for Strong and In-Group Ties

   Pasquetto, Irene V.,  Jahani, Eaman, Atreja, Shubham, and Baum, Matthew

   Proc. ACM Hum.-Comput. Interact. Apr 2022

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   In this paper, we argue that WhatsApp can play an important role in correcting misinformation. We show how specific WhatsApp affordances (flexibility in format and audience selection) and existing social capital (prevalence of strong ties; homophily in political groups) can be leveraged to maximize the re-sharing of debunking messages, such as those accessed by WhatsApp users via ChatBots and Tip-Lines. Debunking messages received in the format of audio files generated more interest and were more effective in correcting beliefs than text- or image-based messages. In addition, we found clear evidence that users re-share debunks at higher rates when they received them from people close to them (strong ties), from individuals who generally agree with them politically (in-group members), or when both conditions are met. We suggest that WhatsApp leverages our findings to maximize the re-share of those fact-checks that are already circulating on the platform by using the existing social capital in the network, unlocking the potential for such debunks to reach a larger audience on WhatsApp.

5. ScamCoins, S*** Posters, and the Search for the Next Bitcoin^TM: Collective Sensemaking in Cryptocurrency Discussions

   Jahani, Eaman, Krafft, Peter, Suhara, Yoshiko, Moro, Esteban, and Pentland, Alex

   Proc. ACM Hum.-Comput. Interact. Nov 2018

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   Participants in cryptocurrency markets are in constant communication with each other about the latest coins and news releases. Do these conversations build hype through the contagiousness of excitement, help the community process information, or play some other role? Using a novel dataset from a major cryptocurrency forum, we conduct an exploratory study of the characteristics of online discussion around cryptocurrencies. Through a regression analysis, we find that coins with more information available and higher levels of technical innovation are associated with higher quality discussion. People who talk about "serious" coins tend to participate in discussion displaying signatures of collective intelligence and information processing, while people who talk about "less serious" coins tend to display signatures of hype and naı̈vety. Interviews with experienced forum members also confirm these quantitative findings. These results highlight the varied roles of discussion in the cryptocurrency ecosystem and suggest that discussion of serious coins may be oriented towards earnest, perhaps more accurate, attempts at discovering which coins are likely to succeed.

6. Improving official statistics in emerging markets using machine learning and mobile phone data

   Jahani, Eaman, Sundsøy, Pal, Bjelland, Johannes, Bengtsson, Linus, Pentland, Alex, and Montjoye, Yves Alexandre

   EPJ Data Science Nov 2017

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   Mobile phones are one of the fastest growing technologies in the developing world with global penetration rates reaching 90%. Mobile phone data, also called CDR, are generated everytime phones are used and recorded by carriers at scale. CDR have generated groundbreaking insights in public health, official statistics, and logistics. However, the fact that most phones in developing countries are prepaid means that the data lacks key information about the user, including gender and other demographic variables. This precludes numerous uses of this data in social science and development economic research. It furthermore severely prevents the development of humanitarian applications such as the use of mobile phone data to target aid towards the most vulnerable groups during crisis. We developed a framework to extract more than 1400 features from standard mobile phone data and used them to predict useful individual characteristics and group estimates. We here present a systematic cross-country study of the applicability of machine learning for dataset augmentation at low cost. We validate our framework by showing how it can be used to reliably predict gender and other information for more than half a million people in two countries. We show how standard machine learning algorithms trained on only 10,000 users are sufficient to predict individual’s gender with an accuracy ranging from 74.3 to 88.4% in a developed country and from 74.5 to 79.7% in a developing country using only metadata. This is significantly higher than previous approaches and, once calibrated, gives highly accurate estimates of gender balance in groups. Performance suffers only marginally if we reduce the training size to 5,000, but significantly decreases in a smaller training set. We finally show that our indicators capture a large range of behavioral traits using factor analysis and that the framework can be used to predict other indicators of vulnerability such as age or socio-economic status. Mobile phone data has a great potential for good and our framework allows this data to be augmented with vulnerability and other information at a fraction of the cost.

Lightly refereed

1. Differential Network Effects on Economic Outcomes: A Structural Perspective

   Jahani, Eaman, Saint-Jacques, Guillaume, Sundsøy, Pal, Bjelland, Johannes, Moro, Esteban, and Pentland, Alex

   In Social Informatics. Nov 2017

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   In a study of about 33,000 individuals in a south Asian country, we find that structural diversity, measured as the fraction of open triads in an ego-network, shows a relatively strong association with individual income. After including all the relevant control variables, the effect of structural diversity becomes exclusive to the highly educated individuals. We hypothesize these results are due to concentrated distribution of economic opportunities among the highly educated social strata combined with homophily among members of the same group. This process leads to two important societal consequences: extra network advantages for the highly educated, similar to the rich club effect, and inadequate diffusion of economic opportunities to the low educated social strata.