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The effectiveness of social media in flood risk communication
Published in Edmund C. Penning-Rowsell, Matilda Becker, Flood Risk Management, 2019
There are some simple but unique features of SM that enables effective communication, one of which is the hashtag on Twitter. A hashtag is composed of a hash symbol (#) followed by any words that categorise the tweet (i.e. message), allowing possible the almost instant search for tweets that share a common topic (Twitter, n.d.). Such a feature is particularly useful in filtering relevant information in flood risk communication (Palen and Hughes, 2018). The hashtag #qldfloods was quickly adopted in Queensland flooding in 2011 by both citizens and government agencies to mark and share flood-related information: more than 35,000 tweets containing the #qldfloods hashtag were generated during the flash floods there, with over a third containing links to further information such as official websites or first-hand photos of the flood (Bruns et al., 2012). During Typhoon Maring in the Philippines in 2014, unified hashtags were suggested, for example #RescuePH, #FloodPH, #ReliefPH, so that people could include these labels in their tweets for more effective searches for information (UNOCHA, 2014).
Social Media in Popular Culture
Published in Michael Muhlmeyer, Shaurya Agarwal, Information Spread in a Social Media Age, 2021
Michael Muhlmeyer, Shaurya Agarwal
Twitter is a social networking site that allows users to post a short limited-character message over the internet via the Twitter website, a dedicated application, or a mobile device such as a cellphone. Twitter posters, or “Tweeters”, will often post a “tweet” concerning what they are doing or thinking. The tweet is often accompanied by a reference tag known as a hashtag. It allows users to view similarly tagged tweets as a collection, usually referencing the same topic, event, or idea. Twitter is also used to post pictures, news, and current events. Many view Twitter as a quick and easy way to discover what is happening around the world by searching relevant keywords for news, trends, or current events.
Feature Engineering for Social Bot Detection
Published in Guozhu Dong, Huan Liu, Feature Engineering for Machine Learning and Data Analytics, 2018
Onur Varol, Clayton A. Davis, Filippo Menczer, Alessandro Flammini
A hashtag is a word prefixed with the hash (#) symbol, and is used in Twitter as a topic identifier. Hashtag co-occurrence networks are weighted, undirected networks with hashtags as the nodes. Two hashtags are linked when they occur together in a given tweet, and the edge is weighted according to the frequency of the co-occurrence in tweets.
Tweet for help: the role of social media in disaster events and the case of the 2015 Mina stampede
Published in Digital Creativity, 2022
Ghada Amoudi, Amal Almansour, Carolyn Watters, Dimah Alahmadi, Fatimah Alruwaili, Sara Alzahrani
The study tracked the top hashtags in the dataset by extracting all the hashtags from the whole dataset, then finding the most frequent ones. Table 4 lists the top ten hashtags and their occurrences. The ‘percentage’ column presents the number of tweets containing the corresponding hashtag. For instance, 22% of all tweets include the hashtag’ تدافع _مشعر _منى #’ meaning ‘#holy_ mina_stampede.’ The frequency of related hashtags ranged between 4% and 22%. As can be observed from the table, there are two different spelling variations of the main hashtag (the first two rows); this phenomenon is commonplace on Twitter, as argued in (Bruns and Burgess 2011). However, according to the study, people on Twitter usually tend to guide other users to use the correct hashtag form. The importance of extracting hashtags from the dataset is twofold. First, hashtags demonstrate the general themes of the discussion, as social media users create hashtags to initiate conversation and draw attention to the post's main topic. Second, knowing the trending hashtags can be utilized to track posts over time, i.e. to monitor the public interest in a particular case and how this may change or decline as time goes by. Twitter hashtags help the user sort and organize their tweets and make them easily accessible to the other user with the same interest. From the user's perspective, it is also vital to find posts relevant to their interests or join communities, as coined by Bruns and Burgess (2011), so they can interact with other Twitter users who share those interests.
Cognitively demanding tasks and the associated learning opportunities within the MathTwitterBlogosphere
Published in International Journal of Mathematical Education in Science and Technology, 2022
Christopher W. Parrish, W. Gary Martin
MTBoS is primarily hosted within the social media platform Twitter and the connected blogs of those who engage with the community. Some MTBoS community members maintain blogs, frequently-updated, personal pages that often relate to mathematics teaching and learning (Nardi et al., 2004). These blogs may include text or web-enhanced reflections on teaching or hyperlinks to relevant resources (Merchant, 2009; Nardi et al., 2004; NCTM, 2014). Given that blogs are not hosted on a common platform, Twitter is often used as a means for like-minded bloggers to connect (Larsen, 2016). Twitter is a social networking website that allows users to share, short 280-character messages, called tweets (McMahon, 2015; Tsukayama, 2017); at the time of the study, tweets could only contain 140 characters. To promote online dialogue, a key feature of Twitter is the hashtag ‘#’; using a hashtag preceding a word or phrase (including no spaces) marks the tweet so that other users can find related tweets (McMahon, 2015). For example, searching ‘#MTBoS’ is one way to find tweets from the MTBoS community related to teaching and learning mathematics; many of these tweets include a link to a related blog post (see tweet on the left in Figure 1), while others do not (see tweet on the right in Figure 1). Figure 2 includes a snapshot of the linked blog post from the tweet on the left in Figure 1.
Revisiting the death of geography in the era of Big Data: the friction of distance in cyberspace and real space
Published in International Journal of Digital Earth, 2018
Su Yeon Han, Ming-Hsiang Tsou, Keith C. Clarke
The first step was to collect Twitter data. Twitter is an Internet-based social networking and microblogging service that allows registered users to post messages up to 140 characters in length. According to the third quarter of 2015 statistics, Twitter has an average of 307 million monthly active users. Twitter Streaming API allows us to collect geo-tagged tweets created within a bounding box that the user specifies. In this study, a bounding box that covers the whole US was set. When users turn on location service functions in their smartphones, the smartphones allow Twitter to exactly locate the users’ locations based on the global position system, Wi-Fi, or triangulated locations from cellphone towers. Geo-tagged tweets contain the coordinate locations of where users were when they posted their messages. It is known that only about 5% of tweets are geo-tagged. To discover knowledge based on the analysis of the tweets, the sample size of tweets should be large enough to be representative of the entire city population. Therefore, to collect sufficient data in terms of volume and scale, we collected tweets for the whole US for about three months from 16 November 2015 to 17 January 2016.