4,348 research outputs found
Negative emotions boost users activity at BBC Forum
We present an empirical study of user activity in online BBC discussion
forums, measured by the number of posts written by individual debaters and the
average sentiment of these posts. Nearly 2.5 million posts from over 18
thousand users were investigated. Scale free distributions were observed for
activity in individual discussion threads as well as for overall activity. The
number of unique users in a thread normalized by the thread length decays with
thread length, suggesting that thread life is sustained by mutual discussions
rather than by independent comments. Automatic sentiment analysis shows that
most posts contain negative emotions and the most active users in individual
threads express predominantly negative sentiments. It follows that the average
emotion of longer threads is more negative and that threads can be sustained by
negative comments. An agent based computer simulation model has been used to
reproduce several essential characteristics of the analyzed system. The model
stresses the role of discussions between users, especially emotionally laden
quarrels between supporters of opposite opinions, and represents many observed
statistics of the forum.Comment: 29 pages, 6 figure
Fact Checking in Community Forums
Community Question Answering (cQA) forums are very popular nowadays, as they
represent effective means for communities around particular topics to share
information. Unfortunately, this information is not always factual. Thus, here
we explore a new dimension in the context of cQA, which has been ignored so
far: checking the veracity of answers to particular questions in cQA forums. As
this is a new problem, we create a specialized dataset for it. We further
propose a novel multi-faceted model, which captures information from the answer
content (what is said and how), from the author profile (who says it), from the
rest of the community forum (where it is said), and from external authoritative
sources of information (external support). Evaluation results show a MAP value
of 86.54, which is 21 points absolute above the baseline.Comment: AAAI-2018; Fact-Checking; Veracity; Community-Question Answering;
Neural Networks; Distributed Representation
Interaction and transformation on social media: the case of Twitter campaigns
The increasing popularity of social media platforms creates new digital social networks in which individuals can interact and share information, news, and opinion. The use of these technologies appears to have the capacity to transform current social configurations and relations, not least within the public and civic spheres. Within the social sciences, much emphasis has been placed on conceptualizing social media’s role in modern society and the interrelationships between online and offline actors and events. In contrast, little attention has been paid to exploring user practices on social media and how individual posts respond to each other. To demonstrate the value of an interactional approach toward social media analysis, we performed a detailed analysis of Twitter-based online campaigns. After categorizing social media posts based on action(s), we developed a typology of user exchanges. We found these social media campaigns to be highly heterogeneous in content, with a wide range of actions performed and substantial numbers of tweets not engaged with the substance of the campaign. We argue that this interactional approach can form the basis for further work conceptualizing the broader impact of activist campaigns and the treatment of social media as “data” more generally. In this way, analytic focus on interactional practices on social media can provide empirical insight into the micro-transformational characteristics within “campaign communication.
Trolls Identification within an Uncertain Framework
The web plays an important role in people's social lives since the emergence
of Web 2.0. It facilitates the interaction between users, gives them the
possibility to freely interact, share and collaborate through social networks,
online communities forums, blogs, wikis and other online collaborative media.
However, an other side of the web is negatively taken such as posting
inflammatory messages. Thus, when dealing with the online communities forums,
the managers seek to always enhance the performance of such platforms. In fact,
to keep the serenity and prohibit the disturbance of the normal atmosphere,
managers always try to novice users against these malicious persons by posting
such message (DO NOT FEED TROLLS). But, this kind of warning is not enough to
reduce this phenomenon. In this context we propose a new approach for detecting
malicious people also called 'Trolls' in order to allow community managers to
take their ability to post online. To be more realistic, our proposal is
defined within an uncertain framework. Based on the assumption consisting on
the trolls' integration in the successful discussion threads, we try to detect
the presence of such malicious users. Indeed, this method is based on a
conflict measure of the belief function theory applied between the different
messages of the thread. In order to show the feasibility and the result of our
approach, we test it in different simulated data.Comment: International Conference on Tools with Artificial Intelligence -
ICTAI , Nov 2014, Limassol, Cypru
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Determining citizens’ opinions about stories in the news media: analysing Google, Facebook and Twitter
We describe a method whereby a governmental policy maker can discover citizens’ reaction to news stories. This is particularly relevant in the political world, where governments’ policy statements are reported by the news media and discussed by citizens. The work here addresses two main questions: whereabouts are citizens discussing a news story, and what are they saying? Our strategy to answer the first question is to find news articles pertaining to the policy statements, then perform internet searches for references to the news articles’ headlines and URLs. We have created a software tool that schedules repeating Google searches for the news articles and collects the results in a database, enabling the user to aggregate and analyse them to produce ranked tables of sites that reference the news articles. Using data mining techniques we can analyse data so that resultant ranking reflects an overall aggregate score, taking into account multiple datasets, and this shows the most relevant places on the internet where the story is discussed. To answer the second question, we introduce the WeGov toolbox as a tool for analysing citizens’ comments and behaviour pertaining to news stories. We first use the tool for identifying social network discussions, using different strategies for Facebook and Twitter. We apply different analysis components to analyse the data to distil the essence of the social network users’ comments, to determine influential users and identify important comments
What Goes Around Comes Around: Learning Sentiments in Online Medical Forums
Currently 19%-28% of Internet users participate in online health discussions. A 2011 survey of the US population estimated that 59% of all adults have looked online for information about health topics such as a specific disease or treatment. Although empirical evidence strongly supports the importance of emotions in health-related messages, there are few studies of the relationship between a subjective lan-guage and online discussions of personal health. In this work, we study sentiments expressed on online medical forums. As well as considering the predominant sentiments expressed in individual posts, we analyze sequences of sentiments in online discussions. Individual posts are classified into one of five categories. We identified three categories as sentimental (encouragement, gratitude, confusion) and two categories as neutral (facts, endorsement). 1438 messages from 130 threads were annotated manually by two annotators with a strong inter-annotator agreement (Fleiss kappa = 0.737 and 0.763 for posts in se-quence and separate posts respectively). The annotated posts were used to analyse sentiments in consec-utive posts. In four multi-class classification problems, we assessed HealthAffect, a domain-specific af-fective lexicon, as well general sentiment lexicons in their ability to represent messages in sentiment recognition
People on Drugs: Credibility of User Statements in Health Communities
Online health communities are a valuable source of information for patients
and physicians. However, such user-generated resources are often plagued by
inaccuracies and misinformation. In this work we propose a method for
automatically establishing the credibility of user-generated medical statements
and the trustworthiness of their authors by exploiting linguistic cues and
distant supervision from expert sources. To this end we introduce a
probabilistic graphical model that jointly learns user trustworthiness,
statement credibility, and language objectivity. We apply this methodology to
the task of extracting rare or unknown side-effects of medical drugs --- this
being one of the problems where large scale non-expert data has the potential
to complement expert medical knowledge. We show that our method can reliably
extract side-effects and filter out false statements, while identifying
trustworthy users that are likely to contribute valuable medical information
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