Characterizing Retweet Bots: The Case of Black Market Accounts
0
Citation
0
Reference
10
Related Paper
Abstract:
Malicious Twitter bots are detrimental to public discourse on social media. Past studies have looked at spammers, fake followers, and astroturfing bots, but retweet bots, which artificially inflate content, are not well understood. In this study, we characterize retweet bots that have been uncovered by purchasing retweets from the black market. We detect whether they are fake or genuine accounts involved in inauthentic activities and what they do in order to appear legitimate. We also analyze their differences from human-controlled accounts. From our findings on the nature and life-cycle of retweet bots, we also point out several inconsistencies between the retweet bots used in this work and bots studied in prior works. Our findings challenge some of the fundamental assumptions related to bots and in particular how to detect them.Microblogging
Interpretability
Hierarchical clustering
Cite
Citations (9)
The unprecedented use of social media through smartphones and other web-enabled mobile devices has enabled the rapid adoption of platforms like Twitter. Event detection has found many applications on the web, including breaking news identification and summarization. The recent increase in the usage of Twitter during crises has attracted researchers to focus on detecting events in tweets. However, current solutions have focused on static Twitter data. The necessity to detect events in a streaming environment during fast paced events such as a crisis presents new opportunities and challenges. In this paper, we investigate event detection in the context of real-time Twitter streams as observed in real-world crises. We highlight the key challenges in this problem: the informal nature of text, and the high volume and high velocity characteristics of Twitter streams. We present a novel approach to address these challenges using single-pass clustering and the compression distance to efficiently detect events in Twitter streams. Through experiments on large Twitter datasets, we demonstrate that the proposed framework is able to detect events in near real-time and can scale to large and noisy Twitter streams.
Microblogging
Identification
Cite
Citations (15)
Microblogging
Cite
Citations (8)
Microblogging
Personalized search
User profile
Similarity (geometry)
Cite
Citations (20)
Microblogging
Tree (set theory)
Cite
Citations (7)
This thesis describes our approach to microblog search personalization. The approach uti-lizes implicit information about the user’s interests to personalize original search results. We first present how do we model users’ preferences on Twitter. Subsequently, we investi-gate different ways to represent search results so that they can be compared with users’ preferences. In addition, we provide a set of personalized strategies and evaluate them in two experiments. Furthermore, we compare the performance of our personalized strategies and further analyze their impacts on the retrieval effectiveness. Our research suggests that our personalized search approach can enhance the retrieval performance on Twitter.
Microblogging
Personalized search
Cite
Citations (0)
Microblogging
Cite
Citations (4)
An overwhelming number of consumers are active in social media platforms. Within these platforms consumers are sharing their true feelings about a particular brand/product, its features, customer service and how it stands the competition. With the booming of microblogs on the Web, people have begun to express their opinions on a wide variety of topics on Twitter and other similar services. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet, “following and “mention relations. We also perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we build a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document. This paper discusses how Twitter data is used as a corpus for analysis by the application of sentiment analysis and a study of different algorithms and methods that help to track influence and impact of a particular user/brand active on the social network.
Sentiment Analysis
Microblogging
Social network (sociolinguistics)
Cite
Citations (20)
Microblogging
Social network (sociolinguistics)
Similarity (geometry)
Cite
Citations (18)
Microblogging
Sentiment Analysis
Leverage (statistics)
Social network (sociolinguistics)
Cite
Citations (3)