An association rule dynamics and classification approach to event detection and tracking in Twitter. (2015)

Twitter is a microblogging application used for sending and retrieving instant\non-line messages of not more than 140 characters. There has been a surge in\nTwitter activities since its launch in 2006 as well as steady increase in event\ndetection research on Twitter data (tweets) in recent years. With 284 million\nmonthly active users Twitter has continued to grow both in size and\nactivity. The network is rapidly changing the way global audience source for\ninformation and influence the process of journalism [Newman, 2009]. Twitter\nis now perceived as an information network in addition to being a social network.\nThis explains why traditional news media follow activities on Twitter\nto enhance their news reports and news updates. Knowing the significance of\nthe network as an information dissemination platform, news media subscribe\nto Twitter accounts where they post their news headlines and include the link to their on-line news where the full story may be found. Twitter users in some\ncases, post breaking news on the network before such news are published by\ntraditional news media. This can be ascribed to Twitter subscribers' nearness\nto location of events. The use of Twitter as a network for information dissemination as well as for\nopinion expression by different entities is now common. This has also brought\nwith it the issue of computational challenges of extracting newsworthy contents\nfrom Twitter noisy data. Considering the enormous volume of data Twitter generates,\nusers append the hashtag (#) symbol as prefix to keywords in tweets.\nHashtag labels describe the content of tweets. The use of hashtags also makes it\neasy to search for and read tweets of interest. The volume of Twitter streaming\ndata makes it imperative to derive Topic Detection and Tracking methods to\nextract newsworthy topics from tweets. Since hashtags describe and enhance the readability of tweets, this research is\ndeveloped to show how the appropriate use of hashtags keywords in tweets can\ndemonstrate temporal evolvements of related topic in real-life and consequently\nenhance Topic Detection and Tracking on Twitter network. We chose to apply\nour method on Twitter network because of the restricted number of characters\nper message and for being a network that allows sharing data publicly. More\nimportantly, our choice was based on the fact that hashtags are an inherent\ncomponent of Twitter. To this end, the aim of this research is to develop, implement and validate a\nnew approach that extracts newsworthy topics from tweets' hashtags of real-life\ntopics over a specified period using Association Rule Mining. We termed our\nnovel methodology Transaction-based Rule Change Mining (TRCM). TRCM\nis a system built on top of the Apriori method of Association Rule Mining to extract patterns of Association Rules changes in tweets hashtag keywords at\ndifferent periods of time and to map the extracted keywords to related real-life\ntopic or scenario. To the best of our knowledge, the adoption of dynamics of\nAssociation Rules of hashtag co-occurrences has not been explored as a Topic\nDetection and Tracking method on Twitter. The application of Apriori to\nhashtags present in tweets at two consecutive period t and t + 1 produces two\nassociation rulesets, which represents rules evolvement in the context of this\nresearch. A change in rules is discovered by matching every rule in ruleset at\ntime t with those in ruleset at time t + 1. The changes are grouped under four\nidentified rules namely 'New' rules, 'Unexpected Consequent' and 'Unexpected\nConditional' rules, 'Emerging' rules and 'Dead' rules. The four rules represent\ndifferent levels of topic real-life evolvements. For example, the emerging rule\nrepresents very important occurrence such as breaking news, while unexpected\nrules represents unexpected twist of event in an on-going topic. The new rule\nrepresents dissimilarity in rules in rulesets at time t and t+1. Finally, the dead\nrule represents topic that is no longer present on the Twitter network. TRCM\nrevealed the dynamics of Association Rules present in tweets and demonstrates\nthe linkage between the different types of rule dynamics to targeted real-life\ntopics/events. In this research, we conducted experimental studies on tweets from different\ndomains such as sports and politics to test the performance effectiveness of our\nmethod. We validated our method, TRCM with carefully chosen ground truth.\nThe outcome of our research experiments include: Identification of 4 rule dynamics in tweets' hashtags namely: New rules,\nEmerging rules, Unexpected rules and 'Dead' rules using Association Rule Mining. These rules signify how news and events evolved in real-life scenario. Identification of rule evolvements on Twitter network using Rule Trend\nAnalysis and Rule Trace.\n Detection and tracking of topic evolvements on Twitter using Transaction-based\nRule Change Mining TRCM.\n Identification of how the peculiar features of each TRCM rules affect their\nperformance effectiveness on real datasets.