Kondenzer: Exploration and visualization of archived social media

Alonso, Omar; Khandelwal, Kartikay

doi:10.1109/icde.2014.6816741

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…In gure, cashtags are color-coded so as to visually highlight companies traded in di erent markets. e most tweeted companies in our dataset are in line with recent trends (e.g., the $BTC (Bitcoin) and $ETH (Ethereum) cryptocurrencies) and with ndings of previous works [3,31] (e.g., $AAPL leading the way, followed by $AMZN, $FB, and $TSLA). Notably, no company from OTCMKTS appears among top mentioned companies, but instead they play a rather marginal role.…”

Section: Analysis Of Stock Microblogssupporting

confidence: 79%

Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Cresci,

Lillo,

Regoli

et al. 2018

Preprint

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Microblogs are increasingly exploited for predicting prices and traded volumes of stocks in nancial markets. However, it has been demonstrated that much of the content shared in microblogging platforms is created and publicized by bots and spammers. Yet, the presence (or lack thereof) and the impact of fake stock microblogs has never systematically been investigated before. Here, we study 9M tweets related to stocks of the 5 main nancial markets in the US. By comparing tweets with nancial data from Google Finance, we highlight important characteristics of Twi er stock microblogs. More importantly, we uncover a malicious practicereferred to as cashtag piggybacking -perpetrated by coordinated groups of bots and likely aimed at promoting low-value stocks by exploiting the popularity of high-value ones. Among the ndings of our study is that as much as 71% of the authors of suspicious nancial tweets are classi ed as bots by a state-of-the-art spambot detection algorithm. Furthermore, 37% of them were suspended by Twi er a few months a er our investigation. Our results call for the adoption of spam and bot detection techniques in all studies and applications that exploit user-generated content for predicting the stock market.

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Section: Analysis Of Stock Microblogssupporting

confidence: 79%

Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Cresci,

Lillo,

Regoli

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Ranking models that capture the temporal scope of queries and documents have been developed in [5,10,25,29]. In addition, there is growing interest in the role of time for search-result snippet generation [1], query classification [16,20], timeline visualization [2,43,44], mining web archives and online communities [35,42], and further tasks in web contents analytics.…”

Section: Prior Work and Backgroundmentioning

confidence: 99%

As Time Goes By

Kuzey

Setty

Strötgen

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

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Temporal expressions (TempEx's for short) are increasingly important in search, question answering, information extraction, and more. Techniques for identifying and normalizing explicit temporal expressions work well, but are not designed for and cannot cope with textual phrases that denote named events, such as "Clinton's term as secretary of state". This paper addresses the problem of detecting such temponyms, inferring their temporal scopes, and mapping them to events in a knowledge base if present there. We present methods for this kind of temponym resolution, using an entity-and TempEx-oriented document model and the Yago knowledge base for distant supervision. We develop a family of Integer Linear Programs for jointly inferring temponym mappings to the timeline and knowledge base. This enriches the document representation and also extends the knowledge base by obtaining new alias names for events. Experiments with three different corpora demonstrate the viability of our methods.

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The World Conversation

Alonso

Bannur

Khandelwal

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

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Over the past couple of years, social networks such as Twitter and Facebook have become the primary source for consuming information on the Internet. One of the main differentiators of this content from traditional information sources available on the Web is the fact that these social networks surface individuals' perspectives. When social media users post and share updates with friends and followers, some of those short fragments of text contain a link and a personal comment about the web page, image or video. We are interested in mining the text around those links for a better understanding of what people are saying about the object they are referring to. Capturing the salient keywords from the crowd is rich metadata that we can use to augment a web page. This metadata can be used for many applications like ranking signals, query augmentation, indexing, and for organizing and categorizing content. In this paper, we present a technique called social signatures that given a link to a web page, pulls the most important keywords from the social chatter around it. That is, a high level representation of the web page from a social media perspective. Our findings indicate that the content of social signatures differs compared to those from a web page and therefore provides new insights. This difference is more prominent as the number of link shares increase. To showcase our work, we present the results of processing a dataset that contains around 1 Billion unique URLs shared in Twitter and Facebook over a two month period. We also provide data points that shed some light on the dynamics of content sharing in social media.

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Kondenzer: Exploration and visualization of archived social media

Cited by 4 publications

References 4 publications

Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

Cashtag piggybacking: uncovering spam and bot activity in stock microblogs on Twitter

As Time Goes By

The World Conversation

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