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1. Give and Take: An End-To-End Investigation of Giveaway Scam Conversion Rates

   https://doi.org/10.1145/3646547.3689005  

   Liu, Enze; Kappos, George; Mugnier, Eric; Invernizzi, Luca; Savage, Stefan; Tao, David; Thomas, Kurt; Voelker, Geoffrey M; Meiklejohn, Sarah (November 2024, Proceedings of the 2024 ACM Internet Measurement Conference)

   The Internet's combination of low communication cost, global reach, and functional anonymity has allowed fraudulent scam volumes to reach new heights. Designing effective interventions requires first understanding the context: how scammers reach potential victims, the earnings they make, and any potential bottlenecks for durable interventions. In this short paper, we focus on these questions in the context of cryptocurrency giveaway scams, where victims are tricked into irreversibly transferring funds to scammers under the pretense of even greater returns. Combining data from Twitter (also known as X), YouTube and Twitch livestreams, landing pages, and cryptocurrency blockchains, we measure how giveaway scams operate at scale. We find that 1 in 1000 scam tweets, and 4 in 100,000 livestream views, net a victim, and that scammers managed to extract nearly $4.62 million from just hundreds of victims during our measurement window. 

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2. Detection of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning Technique

   Lim, J.; Liu, Z.; Zhou, L. (June 2019, IEEE International Conference on Intelligence and Security Informatics)

   Social media has become a powerful and efficient platform for information diffusion. The increasing pervasiveness of social media use, however, has brought about the problems of fraudulent accounts that are intended to diffuse misinformation or malicious contents. Twitter recently released comprehensive archives of fraudulent tweets that are possibly connected to a propaganda effort of Internet Research Agency (IRA) on the 2016 U.S. presidential election. To understand information diffusion in fraudulent networks, we analyze structural properties of the IRA retweet network, and develop deep neural network models to detect fraudulent tweets. The structure analysis reveals key characteristics of the fraudulent network. The experiment results demonstrate the superior performance of the deep learning technique to a traditional classification method in detecting fraudulent tweets. The findings have potential implications for curbing online misinformation. 

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3. To Err.Is Human: Characterizing the Threat of Unintended URLs in Social Media

   https://doi.org/10.14722/ndss.2021.24322  

   Kaleli, Beliz; Kondracki, Brian; Egele, Manuel; Nikiforakis, Nick; Stringhini, Gianluca (January 2021, Proceedings of the 28th Network and Distributed System Security Symposium (NDSS))

   null (Ed.)

   To make their services more user friendly, online social-media platforms automatically identify text that corresponds to URLs and render it as clickable links. In this paper, we show that the techniques used by such services to recognize URLs are often too permissive and can result in unintended URLs being displayed in social network messages. Among others, we show that popular platforms (such as Twitter) will render text as a clickable URL if a user forgets a space after a full stop as the end of a sentence, and the first word of the next sentence happens to be a valid Top Level Domain. Attackers can take advantage of these unintended URLs by registering the corresponding domains and exposing millions of Twitter users to arbitrary malicious content. To characterize the threat that unintended URLs pose to social-media users, we perform a large-scale study of unintended URLs in tweets over a period of 7 months. By designing a classifier capable of differentiating between intended and unintended URLs posted in tweets, we find more than 26K unintended URLs posted by accounts with tens of millions of followers. As part of our study, we also register 45 unintended domains and quantify the traffic that attackers can get by merely registering the right domains at the right time. Finally, due to the severity of our findings, we propose a lightweight browser extension which can, on the fly, analyze the tweets that users compose and alert them of potentially unintended URLs and raise a warning, allowing users to fix their mistake before the tweet is posted. 

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4. #EngineersWeek: Broadening our Understanding of Community Engagement through Analysis of Twitter Use during the National Engineers Week.

   Malik, A; Johri, A; Handa, R; Karbasian, H; Purohit, H. (July 2018, Proceedings of 125th ASEE Annual Conference, Salt Lake City, USA.)

   Community engagement efforts have become an important avenue for raising public interest and know-how related to engineering. These efforts draw the young and the diverse into seeing engineering as a worthwhile profession. One such effort at the national level in the U.S. is the “National Engineers Week”. This is a week-long celebration held every February that consists of numerous events and activities organized for the general public with a focus towards students, women, and under-represented groups. In this paper, we examined this effort through the lens of social media and analyzed Twitter data collected for two hashtags used during the National Engineers Week 2017: “#eweek2017” and “#engineersweek”. Our dataset consisted of 6,583 original tweets and 10,885 retweets. To study the impact of the outreach we used three analytical approaches: descriptive analysis, content analysis, and network analysis. We found that the Twitter campaign participation was dominated by engineering companies and individual users followed by a limited participation of educational institutions, professional engineering associations, and non-profits. As opposed to other popular hashtag campaigns, not a single news media organization was identified as a participating user signaling a lower new media-driven propagation of the campaign among the public. From a content perspective, the tweets can be categorized as event promotion, showcasing employees of engineering companies, or encouraging and inspiring public (especially women and children) towards engineering. With the growing popularity of social media, community engagement efforts need to strategically leverage hashtags and other media elements for a broader impact. 

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5. Analysis of and defense against crowd-retweeting based spam in social networks

   https://doi.org/10.1007/s11280-018-0613-y  

   Liu, Bo; Ni, Zeyang; Luo, Junzhou; Cao, Jiuxin; Ni, Xudong; Liu, Benyuan; Fu, Xinwen (June 2018, World Wide Web)

   Social networking websites with microblogging functionality, such as Twitter or Sina Weibo, have emerged as popular platforms for discovering real-time information on the Web. Like most Internet services, these websites have become the targets of spam campaigns, which contaminate Web contents and damage user experiences. Spam campaigns have become a great threat to social network services. In this paper, we investigate crowd-retweeting spam in Sina Weibo, the counterpart of Twitter in China. We carefully analyze the characteristics of crowd-retweeting spammers in terms of their profile features, social relationships and retweeting behaviors. We find that although these spammers are likely to connect more closely than legitimate users, the underlying social connections of crowd-retweeting campaigns are different from those of other existing spam campaigns because of the unique features of retweets that are spread in a cascade. Based on these findings, we propose retweeting-aware link-based ranking algorithms to infer more suspicious accounts by using identified spammers as seeds. Our evaluation results show that our algorithms are more effective than other link-based strategies. 

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