Catálogo de publicaciones - revistas
ACM Transactions on Knowledge Discovery from Data (TKDD)
Resumen/Descripción – provisto por la editorial en inglés
The ACM Transactions on Knowledge Discovery from Data (TKDD) welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include: scalable and effective algorithms for data mining and data warehousing, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.Palabras clave – provistas por la editorial
No disponibles.
Disponibilidad
| Institución detectada | Período | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No detectada | desde mar. 2007 / hasta dic. 2023 | ACM Digital Library |
Información
Tipo de recurso:
revistas
ISSN impreso
1556-4681
ISSN electrónico
1556-472X
Editor responsable
Association for Computing Machinery (ACM)
País de edición
Estados Unidos
Fecha de publicación
2006-
Cobertura temática
Tabla de contenidos
doi: 10.1145/3070644
Rumor Gauge
Soroush Vosoughi
; Mostafa ‘Neo’ Mohsenvand; Deb Roy
<jats:p>The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations, such as real-world emergencies, can have harmful effects on individuals and society. In this work, we developed models for automated verification of rumors (unverified information) that propagate through Twitter. To predict the veracity of rumors, we identified salient features of rumors by examining three aspects of information spread: linguistic style used to express rumors, characteristics of people involved in propagating information, and network propagation dynamics. The predicted veracity of a time series of these features extracted from a rumor (a collection of tweets) is generated using Hidden Markov Models. The verification algorithm was trained and tested on 209 rumors representing 938,806 tweets collected from real-world events, including the 2013 Boston Marathon bombings, the 2014 Ferguson unrest, and the 2014 Ebola epidemic, and many other rumors about various real-world events reported on popular websites that document public rumors. The algorithm was able to correctly predict the veracity of 75% of the rumors faster than any other public source, including journalists and law enforcement officials. The ability to track rumors and predict their outcomes may have practical applications for news consumers, financial markets, journalists, and emergency services, and more generally to help minimize the impact of false information on Twitter.</jats:p>
Palabras clave: General Computer Science.
Pp. 1-36