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 thatmore »
Segmentation of Tweets with URLs and its Applications to Sentiment Analysis
An important means for disseminating information in social media platforms is by including URLs that point to external sources in user posts. In Twitter, we estimate that about 21% of the daily stream of English-language tweets contain URLs. We notice that NLP tools make little attempt at understanding the relationship between the content of the URL and the text surrounding it in a tweet. In this work, we study the structure of tweets with URLs relative to the content of the Web documents pointed to by the URLs. We identify several segments classes that may appear in a tweet with URLs, such as the title of a Web page and the user's original content. Our goals in this paper are: introduce, define, and analyze the segmentation problem of tweets with URLs, develop an effective algorithm to solve it, and show that our solution can benefit sentiment analysis on Twitter. We also show that the problem is an instance of the block edit distance problem, and thus an NP-hard problem.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Sponsoring Org:
- National Science Foundation
More Like this
Social media data have been used to improve geographic situation awareness in the past decade. Although they have free and openly availability advantages, only a small proportion is related to situation awareness, and reliability or trustworthiness is a challenge. A credibility framework is proposed for Twitter data in the context of disaster situation awareness. The framework is derived from crowdsourcing, which states that errors propagated in volunteered information decrease as the number of contributors increases. In the proposed framework, credibility is hierarchically assessed on two tweet levels. The framework was tested using Hurricane Harvey Twitter data, in which situation awareness related tweets were extracted using a set of predefined keywords including power, shelter, damage, casualty, and flood. For each tweet, text messages and associated URLs were integrated to enhance the information completeness. Events were identified by aggregating tweets based on their topics and spatiotemporal characteristics. Credibility for events was calculated and analyzed against the spatial, temporal, and social impacting scales. This framework has the potential to calculate the evolving credibility in real time, providing users insight on the most important and trustworthy events.
Abstract Twitter is a frequent target for machine learning research and applications. Many problems, such as sentiment analysis, image tagging, and location prediction have been studied on Twitter data. Much of the prior work that addresses these problems within the context of Twitter focuses on a subset of the types of data available, e.g. only text, or text and image. However, a tweet can have several additional components, such as the location and the author, that can also provide useful information for machine learning tasks. In this work, we explore the problem of jointly modeling several tweet components in a common embedding space via task-agnostic representation learning, which can then be used to tackle various machine learning applications. To address this problem, we propose a deep neural network framework that combines text, image, and graph representations to learn joint embeddings for 5 tweet components: body, hashtags, images, user, and location. In our experiments, we use a large dataset of tweets to learn a joint embedding model and use it in multiple tasks to evaluate its performance vs. state-of-the-art baselines specific to each task. Our results show that our proposed generic method has similar or superior performance to specialized application-specific approaches,more »
"This is damn slick!": estimating the impact of tweets on open source project popularity and new contributorsTwitter is widely used by software developers. But how effective are tweets at promoting open source projects? How could one use Twitter to increase a project’s popularity or attract new contributors? In this paper we report on a mixed-methods empirical study of 44,544 tweets containing links to 2,370 open-source GitHub repositories, looking for evidence of causal effects of these tweets on the projects attracting new GitHub stars and contributors, as well as characterizing the high-impact tweets, the people likely being attracted by them, and how they differ from contributors attracted otherwise. Among others, we find that tweets have a statistically significant and practically sizable effect on obtaining new stars and a small average effect on attracting new contributors. The popularity, content of the tweet, as well as the identity of tweet authors all affect the scale of the attraction effect. In addition, our qualitative analysis suggests that forming an active Twitter community for an open source project plays an important role in attracting new committers via tweets. We also report that developers who are new to GitHub or have a long history of Twitter usage but few tweets posted are most likely to be attracted as contributors to the repositoriesmore »
Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checksAs the internet and social media continue to become increasingly used for sharing break- ing news and important updates, it is with great motivation to study the behaviors of online users during crisis events. One of the biggest issues with obtaining information online is the veracity of such content. Given this vulnerability, misinformation becomes a very danger- ous and real threat when spread online. This study investigates misinformation debunking efforts and fills the research gap on cross-platform information sharing when misinforma- tion is spread during disasters. The false rumor “immigration status is checked at shelters” spread in both Hurricane Harvey and Hurricane Irma in 2017 and was analyzed in this paper based on a collection of 12,900 tweets. By studying the rumor control efforts made by thousands of accounts, we found that Twitter users respond and interact the most with tweets from verified Twitter accounts, and especially government organizations. Results on sourcing analysis show that the majority of Twitter users who utilize URLs in their post- ings are employing the information in the URLs to help debunk the false rumor. The most frequently cited information comes from news agencies when analyzing both URLs and domains. This paper provides novel insightsmore »