Please Sign to Save... : How Online EnvironmentalPetitions Succeed

Social media have become one of the key platforms to support the debate on climate change. In particular, Twitter allows easy information dissemination when running environmental campaigns. Yet, the dynamics of these campaigns on social platforms still remain largely unexplored. In this paper, we study the success factors enabling online petitions to attain their required number of signatures. We present an analysis of e-petitions and identify how their number of users, tweets and retweets correlate with their success. In addition, we show that environmental petitions are actively promoted by popular public campaigns on Twitter. Finally, we present an annotated corpus of petitions posted by environmental campaigns together with their corresponding tweets to enable further exploration

Analyzing Large-Scale Public Campaigns on Twitter

Social media has become an important instrument for running various types of public campaigns and mobilizing people. Yet, the dynamics of public campaigns on social networking platforms still remain largely unexplored. In this paper, we present an in-depth analysis of over one hundred large-scale campaigns on social media platforms covering more than 6 years. In particular, we focus on campaigns related to climate change on Twitter, which promote online activism to encourage, educate, and motivate people to react to the various issues raised by climate change. We propose a generic framework based on a crowdsourcing to identify both the type of a given campaign as well as the various actions undertaken throughout its lifespan: official meetings, physical actions, calls for action, publications on climate related research, etc. We study whether the type of a campaign is correlated to the actions undertaken and how these actions influence the flow of the campaign. Leveraging more than one hundred different campaigns, we build a model capable of accurately predicting the presence of individual actions in tweets. Finally, we explore the influence of active users on the overall campaign flow

Efficient document filtering using vector space topic expansion and pattern-mining: the case of event detection in microposts

Automatically extracting information from social media is challenging given that social content is often noisy, ambiguous, and inconsistent. However, as many stories break on social channels first before being picked up by mainstream media, developing methods to better handle social content is of utmost importance. In this paper, we propose a robust and effective approach to automatically identify microposts related to a specific topic defined by a small sample of reference documents. Our framework extracts clusters of semantically similar microposts that overlap with the reference documents, by extracting combinations of key features that define those clusters through frequent pattern mining. This allows us to construct compact and interpretable representations of the topic, dramatically decreasing the computational burden compared to classical clustering and k-NN-based machine learning techniques and producing highly-competitive results even with small training sets (less than 1'000 training objects). Our method is efficient and scales gracefully with large sets of incoming microposts. We experimentally validate our approach on a large corpus of over 60M microposts, showing that it significantly outperforms state-of-the-art techniques

Template Induction over Unstructured Email Corpora

Unsupervised template induction over email data is a central component in applications such as information extraction, document classification, and auto-reply. The benefits of automatically generating such templates are known for structured data, e.g. machine generated HTML emails. However much less work has been done in performing the same task over unstructured email data. We propose a technique for inducing high quality templates from plain text emails at scale based on the suffix array data structure. We evaluate this method against an industry-standard approach for finding similar content based on shingling, running both algorithms over two corpora: a synthetically created email corpus for a high level of experimental control, as well as user-generated emails from the well-known Enron email corpus. Our experimental results show that the proposed method is more robust to variations in cluster quality than the baseline and templates contain more text from the emails, which would benefit extraction tasks by identifying transient parts of the emails. Our study indicates templates induced using suffix arrays contain approximately half as much noise (measured as entropy) as templates induced using shingling. Furthermore, the suffix array approach is substantially more scalable, proving to be an order of magnitude faster than shingling even for modestly-sized training clusters. Public corpus analysis shows that email clusters contain on average 4 segments of common phrases, where each of the segments contains on average 9 words, thus showing that templatization could help users reduce the email writing effort by an average of 35 words per email in an assistance or auto-reply related task

AI for social good: unlocking the opportunity for positive impact

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good

Please Sign to Save... : How Online Environmental Petitions Succeed

Social media have become one of the key platforms to support the debate on climate change. In particular, Twitter allows easy information dissemination when running environmental campaigns. Yet, the dynamics of these campaigns on social platforms still remain largely unexplored. In this paper, we study the success factors enabling online petitions to attain their required number of signatures. We present an analysis of e-petitions and identify how their number of users, tweets and retweets correlate with their success. In addition, we show that environmental petitions are actively promoted by popular public campaigns on Twitter. Finally, we present an annotated corpus of petitions posted by environmental campaigns together with their corresponding tweets to enable further exploration

Diffusion Entropy and the Path Dimension of Frictional Finger Patterns

The authors investigate, using both analytical and numerical methods, the entropy associated with a diffusion process inside frictional finger patterns. The entropy obtained from simulations of diffusion inside the pattern is compared to analytical predictions based on an effective continuum description. The analytical result predicts that the entropy depends in a particular way on the path dimension of the system, which governs the scaling of simple paths in the system. The findings indicates that there is a close analogy between the frictional fingers in the continuum and minimum spaning trees on the lattice, as the path dimension is found, through studies of the entropy, to be close to the defining value for the minimum spanning tree universality class