Ahmet Aker

Detection and Resolution of Rumours in Social Media: A Survey

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

Analyzing the capabilities of crowdsourcing services for text summarization

This paper presents a detailed analysis of the use of crowdsourcing services for the Text Summarization task in the context of the tourist domain. In particular, our aim is to retrieve relevant information about a place or an object pictured in an image in order to provide a short summary which will be of great help for a tourist. For tackling this task, we proposed a broad set of experiments using crowdsourcing services that could be useful as a reference for others who want to rely also on crowdsourcing. From the analysis carried out through our experimental setup and the results obtained, we can conclude that although crowdsourcing services were not good to simply gather gold-standard summaries (i.e., from the results obtained for experiments 1, 2 and 4), the encouraging results obtained in the third and sixth experiments motivate us to strongly believe that they can be successfully employed for finding some patterns of behaviour humans have when generating summaries, and for validating and checking other tasks. Furthermore, this analysis serves as a guideline for the types of experiments that might or might not work when using crowdsourcing in the context of text summarization.

Automatic image captioning from the web for GPS photographs

Increasing quantities of images are indexed by GPS coordinates. However, it is difficult to search within such pictures. In this paper, we propose a solution to automatically generate captions (including place name, keywords and summary) from the web content based on image location information. The richer descriptions have great potential to help image organisation, indexing and search. The solution is realised through the synergetic techniques from Geographic Information System, Web IR and multi-document summarisation.

A Graph-Based Approach to Topic Clustering for Online Comments to News

This paper investigates graph-based approaches to labeled topic clustering of reader comments in online news. For graph-based clustering we propose a linear regression model of similarity between the graph nodes (comments) based on similarity features and weights trained using automatically derived training data. To label the clusters our graph-based approach makes use of DBPedia to abstract topics extracted from the clusters. We evaluate the clustering approach against gold standard data created by human annotators and compare its results against LDA – currently reported as the best method for the news comment clustering task. Evaluation of cluster labelling is set up as a retrieval task, where human annotators are asked to identify the best cluster given a cluster label. Our clustering approach significantly outperforms the LDA baseline and our evaluation of abstract cluster labels shows that graph-based approaches are a promising method of creating labeled clusters of news comments, although we still find cases where the automatically generated abstractive labels are insufficient to allow humans to correctly associate a label with its cluster.

STARLET: Multi-document Summarization of Service and Product Reviews with Balanced Rating Distributions

Reviews about products and services are abundantly available online. However, selecting information relevant to a potential buyer involves a significant amount of time reading users reviews and weeding out comments unrelated to the important aspects of the reviewed entity. In this work, we present STARLET, a novel approach to multi-document summarization for evaluative text that considers the rating distribution as summarization feature to consistently preserve the overall opinion distribution expressed in the original reviews. We demonstrate how this method improves traditional summarization techniques and leads to more readable summaries.

Improving automatic image captioning using text summarization techniques

This paper presents two different approaches to automatic captioning of geo-tagged images by summarizing multiple web-documents that contain information related to an images location: a graph-based and a statistical-based approach. The graph-based method uses text cohesion techniques to identify information relevant to a location. The statistical-based technique relies on different word or noun phrases frequency counting for identifying pieces of information relevant to a location. Our results show that summaries generated using these two approaches lead indeed to higher ROUGE scores than n-gram language models reported in previous work.

The SENSEI Annotated Corpus: Human Summaries of Reader Comment Conversations in On-line News

Researchers are beginning to explore how to generate summaries of extended argumentative conversations in social media, such as those found in reader comments in on-line news. To date, however, there has been little discussion of what these summaries should be like and a lack of humanauthored exemplars, quite likely because writing summaries of this kind of interchange is so difficult. In this paper we propose one type of reader comment summary – the conversation overview summary – that aims to capture the key argumentative content of a reader comment conversation. We describe a method we have developed to support humans in authoring conversation overview summaries and present a publicly available corpus – the first of its kind – of news articles plus comment sets, each multiply annotated, according to our method, with conversation overview summaries.

A Poodle or a Dog? Evaluating Automatic Image Annotation Using Human Descriptions at Different Levels of Granularity

Different people may describe the same object in different ways, and at varied levels of granularity (“poodle”, “dog”, “pet” or “animal”?) In this paper, we propose the idea of ‘granularityaware’ groupings where semantically related concepts are grouped across different levels of granularity to capture the variation in how different people describe the same image content. The idea is demonstrated in the task of automatic image annotation, where these semantic groupings are used to alter the results of image annotation in a manner that affords different insights from its initial, category-independent rankings. The semantic groupings are also incorporated during evaluation against image descriptions written by humans. Our experiments show that semantic groupings result in image annotations that are more informative and flexible than without groupings, although being too flexible may result in image annotations that are less informative.

Comment-to-Article Linking in the Online News Domain

Online commenting to news articles provides a communication channel between media professionals and readers offering a crucial tool for opinion exchange and freedom of expression. Currently, comments are detached from the news article and thus removed from the context that they were written for. In this work, we propose a method to connect readers’ comments to the news article segments they refer to. We use similarity features to link comments to relevant article segments and evaluate both word-based and term-based vector spaces. Our results are comparable to state-of-theart topic modeling techniques when used for linking tasks. We demonstrate that article segments and comments representation are relevant to linking accuracy since we achieve better performances when similarity features are computed using similarity between terms rather than words.

Do humans have conceptual models about geographic objects? A user study

In this article, we investigate what sorts of information humans request about geographical objects of the same type. For example, Edinburgh Castle and Bodiam Castle are two objects of the same type: “castle.” The question is whether specific information is requested for the object type “castle” and how this information differs for objects of other types (e.g., church, museum, or lake). We aim to answer this question using an online survey. In the survey, we showed 184 participants 200 images pertaining to urban and rural objects and asked them to write questions for which they would like to know the answers when seeing those objects. Our analysis of the 6,169 questions collected in the survey shows that humans have shared ideas of what to ask about geographical objects. When the object types resemble each other (e.g., church and temple), the requested information is similar for the objects of these types. Otherwise, the information is specific to an object type. Our results may be very useful in guiding Natural Language Processing tasks involving automatic generation of templates for image descriptions and their assessment, as well as image indexing and organization.