Rule Type Identification Using TRCM for Trend Analysis in Twitter

Gomes, João Bártolo; Adedoyin-Olowe, Mariam; Gaber, Mohamed Medhat; Stahl, Frederic

doi:10.1007/978-3-319-02621-3_20

Cited by 7 publications

(9 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our previous work [12,13] we analysed hashtag keywords in tweets on the 30 same topic at 2 consecutive time periods using Association Rule Mining (ARM) and Transaction-based Rule Change Mining (TRCM). Our TRCM method was able to identify 4 temporal Association Rules (ARs) relating to evolving concept of tweets.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A rule dynamics approach to event detection in Twitter with its application to sports and politics

Adedoyin-Olowe

Gaber

Dancausa

et al. 2016

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

“…The identified ARs are namely; "New rules", "Emerging rules", "Unexpected Consequent/Conditional rules" and "Dead rules". The 35 results of our previous experiments [12,13] relates the identified ARs to evolving events in real life. To maintain coherence in this paper, ARM, ARs and TRCM concepts will be explained in subsequent sections.…”

Section: Introductionmentioning

confidence: 99%

A rule dynamics approach to event detection in Twitter with its application to sports and politics

Adedoyin-Olowe

Gaber

Dancausa

et al. 2016

Expert Systems with Applications

Self Cite

View full text Add to dashboard Cite

“…TDT is receiving high level of attention recently. Many researchers and authors are conducting experiments on TDT on social network sites, especially on Twitter [32]; [33]; [34]; [35]; [36]; [37]; [38]. In [32] the abruptness in hashtags usage is labeled unexpected rule evolvement which is demonstrated by TRCM (Transaction-based Rule Change Mining).…”

Section: F Topic Detection and Tracking On Social Networkmentioning

confidence: 99%

The Path to Ethical and Moral Use of Media Tools in Data Mining for Social Networks

Muli¹,

Mutua²

2018

IJIRCST

View full text Add to dashboard Cite

The development of newer and easy to use ICT applications especially those that allow social interactions has led to in-creased user levels and experiences. It amazes to see the young operating a smart phone with no challenges unlike their parents. Although these applications have come of age, it is important to address the moral and ethical issues that have emanated with these new applications. This paper addresses moral and ethical issues related to various applications in ICT. It specifically identities personal morals and ethics as the main agents of inculcating moral and ethical behaviors in the development of and usage of ICT related applications. It discusses the role played by morals and ethical behavior in ICT, Social networking sites and tools, and mobile phone applications (mobile apps). It recognizes the role of application developers and general users of ICT in instilling ethical behaviors among themselves and to other remote users. In conclusion, it identifies the critical roles of all types of users and proposes that all should be guided by their personal morals and ethical values that define their characters.

show abstract

“…We also compute the burst in the volume of tweets posted by the teams' fans shortly after each goal. We extend our experiments in [1], [9] by applying TRCM on a dynamic sport datasets; the English FA Cup Finals 2012. Our method first detects frequent hashtags present in the related tweets, which we then apply as parameter for TDT as contained in the ground truth source utilized.…”

Section: Introductionmentioning

confidence: 99%

“…Rule MatchingIn our previous experiments[1] and[9] we used Rule Matching (RM) to detect change in patterns of rules in r t i and r t+1 j , where t is the time and i/j are the rules present in the tweets. Rules in r t+1 j are matched against rules in r t i to detect patterns of rule changes.…”

mentioning

confidence: 99%

Extraction of Unexpected Rules from Twitter Hashtags and its Application to Sport Events

Adedoyin-Olowe

Gaber

Dancausa

et al. 2014

2014 13th International Conference on Machine Learning and Applications

Self Cite

View full text Add to dashboard Cite

Twitter has become a dependable microblogging tool for real time information dissemination and newsworthy events broadcast. Its users sometimes break news on the network faster than traditional newsagents due to their presence at ongoing real life events at most times. Different topic detection methods are currently used to match Twitter posts to real life news of mainstream media. In this paper, we analyse tweets relating to the English FA Cup finals 2012 by applying our novel method named TRCM to extract association rules present in hashtag keywords of tweets in different time-slots. Our system identify evolving hashtag keywords with strong association rules in each time-slot. We then map the identified hashtag keywords to event highlights of the game as reported in the ground truth of the main stream media. The performance effectiveness measure of our experiments show that our method perform well as a Topic Detection and Tracking approach.

show abstract

Rule Type Identification Using TRCM for Trend Analysis in Twitter

Cited by 7 publications

References 4 publications

A rule dynamics approach to event detection in Twitter with its application to sports and politics

A rule dynamics approach to event detection in Twitter with its application to sports and politics

The Path to Ethical and Moral Use of Media Tools in Data Mining for Social Networks

Extraction of Unexpected Rules from Twitter Hashtags and its Application to Sport Events

Contact Info

Product

Resources

About