Methods are proposed for the detection of events which are deemed to be "controversial" in the sense that they divide opinion amongst large groups of people. The research introduces the notion of a Twitter snapshot which looks at (1) a target entity (2) a set time period and (3) a set of tweets about the entity during the time period. Then, the snapshots are assigned a controversy score and ranked according to their controversy score. The main focus is controversial event detection, where the event involves celebrities; and this is done through the modelling of a complex, rich, feature space focussed on behaviour variables around tweets. Then, a supervised learning model is trained with this feature space using data that has been prepared by human editors to classify events that happen to be controversial.
The tracking of citizens' reactions in social media during crises has attracted an increasing level of interest in the research community. In particular, sentiment analysis over social media posts can be regarded as a particularly useful tool, enabling civil protection and law enforcement agencies to more effectively respond during this type of situation. Prior work on sentiment analysis in social media during crises has applied well-known techniques for overall sentiment detection in posts. However, we argue that sentiment analysis of the overall post might not always be suitable, as it may miss the presence of more targeted sentiments, e.g. about the people and organizations involved (which we refer to as sentiment targets). Through a crowdsourcing study, we show that there are marked differences between the overall tweet sentiment and the sentiment expressed towards the subjects mentioned in tweets related to three crises events.
Companies and financial investors are paying increasing attention to social consciousness in developing their corporate strategies and making investment decisions to support a sustainable economy for the future. Public discussion on incidents and events -- controversies -- of companies can provide valuable insights on how well the company operates with regards to social consciousness and indicate the company's overall operational capability. However, there are challenges in evaluating the degree of a company's social consciousness and environmental sustainability due to the lack of systematic data. We introduce a system that utilizes Twitter data to detect and monitor controversial events and show their impact on market volatility. In our study, controversial events are identified from clustered tweets that share the same 5W terms and sentiment polarities of these clusters. Credible news links inside the event tweets are used to validate the truth of the event. A case study on the Starbucks Philadelphia arrests shows that this method can provide the desired functionality.
Twitter, as a popular microblogging service, has become a new information channel for users to receive and exchange the mostup-to-date information on current events. However, since there is no control on how users can publish messages on Twitter, finding newsworthy events from Twitter becomes a difficult task like "finding a needle in a haystack". In this paper we propose a general unsupervised framework to explore events from tweets, which consists of a pipeline process of filtering, extraction and categorization. To filter out noisy tweets, the filtering step exploits a lexicon-based approach to separate tweets that are event-related from those that are not. Then, based on these event-related tweets, the structured representations of events are extracted and categorized automatically using an unsupervised Bayesian model without the use of any labelled data. Moreover, the categorized events are assigned with the event type labels without human intervention. The proposed framework has been evaluated on over 60 millions tweets which were collected for one month in December 2010. A precision of 70.49% is achieved in event extraction, outperforming a competitive baseline by nearly 6%. Events are also clustered into coherence groups with the automatically assigned event type label.
Due to the vast amount of user-generated content in the emerging Web 2.0, there is a growing need for computational processing of sentiment analysis in documents. Most of the current research in this field is devoted to product reviews from websites. Microblogs and social networks pose even a greater challenge to sentiment classification. However, especially marketing and political campaigns leverage from opinions expressed on Twitter or other social communication platforms. The objects of interest in this paper are the presidential candidates of the Republican Party in the USA and their campaign topics. In this paper we introduce the combination of the noun phrases’ frequency and their PMI measure as constraint on aspect extraction. This compensates for sparse phrases receiving a higher score than those composed of high-frequency words. Evaluation shows that the meronymy relationship between politicians and their topics holds and improves accuracy of aspect extraction.