Harvesting Social Signals to Inform Peace Processes Implementation and Monitoring

Nigam, Aastha; Dambanemuya, Henry K.; Joshi, Madhav; Chawla, Nitesh V.

doi:10.1089/big.2017.0055

Cited by 12 publications

(7 citation statements)

References 36 publications

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“…We demonstrate how social media provides large volumes of information that can be leveraged in conjunction with polls, to better understand the success or failure of a peace process. Beyond peace processes, this work also highlights how public opinion can be of key importance to other sociopolitical events such as military operations and immigration [6].…”

Section: Social Signals To Inform Peace Processes Implementation and mentioning

confidence: 96%

Beyond Who and What

Nigam

2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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Social media and technology have drastically transformed the social and information networks around us. They have impacted how we communicate with others, search for information, and even how we express our personal opinions. Further, in this era of big data, not only are the online services collecting vast variety of user data, but we, as users, are also readily divulging significant amounts of information. Together, massive datasets obtained from diverse sources such as organizations and user generated content give us the opportunity to explore and understand complex behavior of both individuals and communities. This proposal aims at designing generalizable and scalable data-driven frameworks to gain a deeper understanding of the users, explain their actions and preferences, and infer personal traits. The proposed models will enable us to move beyond asking the conventional questions of who and what, and reveal answers about how and why. Given the varying digital persona of users motivated by their personal preferences and social attributes, we characterize users in two distinct domains: online health and peace studies. The models are designed to solve various real-world challenges to maximize their broader impact. CCS CONCEPTS • Information systems → Data mining; Web searching and information discovery; • Computing methodologies → Machine learning; • Social and professional topics → User characteristics; • Applied computing → Law, social and behavioral sciences;

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Section: Social Signals To Inform Peace Processes Implementation and mentioning

confidence: 96%

Beyond Who and What

Nigam

2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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“…One example of digital spoilers can be found in Colombia, where the opponents of the peace agreement promoted strong and negatively charged hashtags on social media concerning the endorsement of the peace process with the FARC guerilla organization in October 2016 (Nigam et al, 2017). The promotion of these messages, among other factors, affected the perception of the peace negotiations, which was reflected in the rejection of the peace plebiscite by a small margin.…”

Section: Redefining Stability In Cyberspacementioning

confidence: 99%

Cyber Peace

2022

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The international community is too often focused on responding to the latest cyber-attack instead of addressing the reality of pervasive and persistent cyber conflict. From ransomware against the city government of Baltimore to state-sponsored campaigns targeting electrical grids in Ukraine and the U.S., we seem to have relatively little bandwidth left over to ask what we can hope for in terms of 'peace' on the Internet, and how to get there. It's also important to identify the long-term implications for such pervasive cyber insecurity across the public and private sectors, and how they can be curtailed. This edited volume analyzes the history and evolution of cyber peace and reviews recent international efforts aimed at promoting it, providing recommendations for students, practitioners and policymakers seeking an understanding of the complexity of international law and international relations involved in cyber peace. This title is also available as Open Access on Cambridge Core.

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“…The increasing availability of unstructured text data and computing power has led to advances in information extraction, natural language processing, and knowledge representation with important applications such as characterizing scientific production (Murdock, Allen, and DeDeo 2017), conversations about climate change (Roxburgh et al 2019), and civil discourse in peace processes (Nigam et al 2017). In the recent past, studies of online media during pandemics have grown in popularity because the ability to detect, monitor, and predict disease trends can allow governments and health organizations to plan and allocate resources for treatment and prevention more effectively.…”

Section: Studies Of Online Media During Pandemicsmentioning

confidence: 99%

Characterizing Online Media on COVID-19 during the Early Months of the Pandemic

Dambanemuya¹,

Lin

Horvát³

2021

JQD

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The 2019 coronavirus disease had wide-ranging effects on public health throughout the world. Vital in managing its spread was effective communication about public health guidelines such as social distancing and sheltering in place. Our study provides a descriptive analysis of online information sharing about coronavirus-related topics in 5.2 million English-language news articles, blog posts, and discussion forum entries shared in 197 countries during the early months of the pandemic. We illustrate potential approaches to analyze the data while emphasizing how often-overlooked dimensions of the online media environment play a crucial role in the observed information-sharing patterns. In particular, we show how the following three dimensions matter: (1) online media posts’ geographic location in relation to local exposure to the virus; (2) the platforms and types of media chosen for discussing various topics; and (3) temporal variations in information-sharing patterns. Our descriptive analyses of the multimedia data suggest that studies that overlook these crucial aspects of online media may arrive at misleading conclusions about the observed information-sharing patterns. This could impact the success of potential communication strategies devised based on data from online media. Our work has broad implications for the study and design of computational approaches for characterizing large-scale information dissemination during pandemics and beyond.

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Harvesting Social Signals to Inform Peace Processes Implementation and Monitoring

Cited by 12 publications

References 36 publications

Beyond Who and What

Beyond Who and What

Cyber Peace

Characterizing Online Media on COVID-19 during the Early Months of the Pandemic

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