3,872 research outputs found
Social media analytics: a survey of techniques, tools and platforms
This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing
Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach
The ever-growing number of people using Twitter makes it a valuable source of
timely information. However, detecting events in Twitter is a difficult task,
because tweets that report interesting events are overwhelmed by a large volume
of tweets on unrelated topics. Existing methods focus on the textual content of
tweets and ignore the social aspect of Twitter. In this paper we propose MABED
(i.e. mention-anomaly-based event detection), a novel statistical method that
relies solely on tweets and leverages the creation frequency of dynamic links
(i.e. mentions) that users insert in tweets to detect significant events and
estimate the magnitude of their impact over the crowd. MABED also differs from
the literature in that it dynamically estimates the period of time during which
each event is discussed, rather than assuming a predefined fixed duration for
all events. The experiments we conducted on both English and French Twitter
data show that the mention-anomaly-based approach leads to more accurate event
detection and improved robustness in presence of noisy Twitter content.
Qualitatively speaking, we find that MABED helps with the interpretation of
detected events by providing clear textual descriptions and precise temporal
descriptions. We also show how MABED can help understanding users' interest.
Furthermore, we describe three visualizations designed to favor an efficient
exploration of the detected events.Comment: 17 page
Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis
Notwithstanding recent work which has demonstrated the potential of using
Twitter messages for content-specific data mining and analysis, the depth of
such analysis is inherently limited by the scarcity of data imposed by the 140
character tweet limit. In this paper we describe a novel approach for targeted
knowledge exploration which uses tweet content analysis as a preliminary step.
This step is used to bootstrap more sophisticated data collection from directly
related but much richer content sources. In particular we demonstrate that
valuable information can be collected by following URLs included in tweets. We
automatically extract content from the corresponding web pages and treating
each web page as a document linked to the original tweet show how a temporal
topic model based on a hierarchical Dirichlet process can be used to track the
evolution of a complex topic structure of a Twitter community. Using
autism-related tweets we demonstrate that our method is capable of capturing a
much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, 201
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