Archived project

MineSocMed

Goal: Mining Social Media, a project funded by the Academy of Finland, running 2013-2017. Budget 700000 euro.

Methods: Text Mining, Sentiment Analysis

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Project log

Boyang Zhang
added 3 research items
This research explores the relation between a crisis and public discussion on related issues. In organisational crisis communication a single-issue strategy is often proposed. Such a strategy, however, may not be adequate in complex crises where the crisis lifecycle is likely to encompass shorter lifecycles of issues that generate attention. Decomposing the online crisis debate into a pattern of issues supports understanding of public perceptions, and hence of crisis response and communication. This is investigated through an analysis of Facebook posts prompted by the loss of Malaysia Airlines flight MH370 in 2014. The analysis shows that during the crisis a variety of related issues arose that became topics of public debate. Compassion for victims dominated in the early stages of the crisis, while later on reputation-related issues took over. The insights gained help in understanding the results of social media monitoring during complex organisational crises and facilitate organisational decision making.
Denis Kotkov
added a research item
Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. According to our results, the source domain can improve serendipity in the target domain for both approaches. The source domain decreases accuracy for content-based filtering and increases accuracy for collaborative filtering. The improvement of accuracy decreases with the growth of non-overlapping items in different domains.
Boyang Zhang
added 2 research items
Purpose – The purpose of this paper is to clarify the aims, monitoring methods and challenges of social media monitoring from the perspective of international companies. Trends in the literature are also investigated. Design/methodology/approach – Based on a systematic literature review, 30 key articles from 2008 to 2012 were further analysed. Findings – International companies need real-time monitoring software, expertise and dynamic visualization to facilitate early detection and prognoses supporting strategy making. This is a costly affair, prompting questions about return on investment. A recent trend in the research literature concerns the development of models describing how issues spread in social media with the aim of facilitating prognoses. Research limitations/implications – The online databases used comprised refereed peer-reviewed scientific articles. Books were not included in the search process. Practical implications – Because information spreads fast in social media and affects international companies, they need to identify issues early, in order to monitor and predict their growth. This paper discusses the difficulties posed by this objective. Originality/value – Social media monitoring is a young research area and research on the topic has been conducted from many different perspectives. Therefore, this paper brings together current insights geared towards corporate communication by international companies.
available in open access here http://ojcmt.net/articles/51/516.pdf
Jari Veijalainen
added 14 research items
This paper describes a hierarchical, three-level modelling framework for monitoring social media. Immediate social reality is modelled through the first level of the models. They represent various virtual communities at social media sites and adhere to the social world models of the sites, i.e., the "site ontologies". The second-level model is a temporal multirelational graph that captures the static and dynamic properties of the first-level models from the perspective of the monitoring site. The third-level model consists of a temporal relational database scheme that models the temporal multirelational graph within the database. The models are specified and instantiated at the monitoring site. An important contribution of the paper is the description of the mappings between the modelling levels and their schematic algorithmic implementation within the monitoring site. The paper also describes theoretical limits for the accuracy and timeliness of monitoring activity, assuming that the monitoring is performed remotely over the internet.
The purpose of this paper is to gain understanding of what factors cause rapid issue spread in social media, to help predict issue growth. The frequency graphics of two issues, Arctic Sunrise and U.S. capitol shooting, were compared to investigate rapidity of spread on Twitter. Next, a qualitative model was applied to explain the differences found. Furthermore, a first attempt was made to investigate issue transfer between social media and news media. The findings showed that news items and tweets were interrelated, with hardly any time-lag in between, although the tweets continued longer and included more emotion. When in practice monitoring social media, attention should be given to issue characteristics that relate to drives to forward information. Emergencies with eye-witnesses present have considerable potential to engage users in social media interactions, while other issues require more organizational resources and engaging influentials to facilitate issue growth.
This paper describes traces of user activity around a alleged online social network profile of a Boston Marathon bombing suspect, after the tragedy occurred. The analyzed data, collected with the help of an automatic social media monitoring software, includes the perpetrator's page saved at the time the bombing suspects' names were made public, and the subsequently appearing comments left on that page by other users. The analyses suggest that a timely protection of online media records of a criminal could help prevent a large-scale public spread of communication exchange pertaining to the suspects/criminals' ideas, messages, and connections.
Jari Veijalainen
added a project goal
Mining Social Media, a project funded by the Academy of Finland, running 2013-2017. Budget 700000 euro.