What is Twitter

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches, and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories.

Predictive Analysis on Twitter: Techniques and Applications

With the rapid growth of online social media content, and the impact these have made on people’s behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as “quantification.” By the term “quantification,” we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.

Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer

Microblogs such as Twitter are characterized by the richness and recency of information shared by their users during major events. However, it is very challenging to automatically mine for information or for users sharing certain information due to the huge variety of unstructured stream of data shared in such microblogs. This work proposes a ranking and classification model for identifying users sharing useful information during a specified event. The model is based on a novel set of features that can be computed in real time. These features are designed such that they take into account both the on and off-topic activities of a user. Once users are characterized by a feature vector, supervised machine learning tool is trained to classify users as either prominent or not. Our model has been tested on data shared during a flooding disaster event and performed very well. The achieved results show the effectiveness of the proposed model for both the classification and ranking of prominent users in such events, and also the importance of the adjustment of the on-topic features by the off-topic ones when describing users' activities.

/pdf/prominent-users-detection-during-specific-events-by-learning-2y28227l8o.pdf

Prominent Users Detection during Specific Events by Learning On- and Off-topic Features of User Activities

Obtaining relevant timely information during crisis events is a challenging task that can be fundamental to handle the consequences deriving from both unexpected events (e.g., terrorist attacks) and partially predictable ones (i.e., natural disasters). Even though microblogging-based online social networks (e.g., Twitter) have become an attractive data source in these emergency situations, overcoming the information overload deriving from mass events is not trivial. The aim of this work was to enable unsupervised extraction of relevant information from Twitter data during a crisis event, offering a lightweight alternative to learning-based approaches. The proposed lightweight crisis management framework integrates natural language processing and clustering techniques in order to produce a ranking of tweets relevant to a crisis situation based on their informativeness. Experiments carried out on six Twitter collections in two languages (English and French) proved the significance and the flexibility of our approach.

A lightweight and multilingual framework for crisis information extraction from Twitter data

Microblogs have proved their potential to attract people from all over the world to express voluntarily what is happening around them during unexpected events. However, retrieving relevant information from the huge amount of data shared in real time in these microblogs remain complex. This paper proposes a new system named MASIR for real-time information retrieval from microblogs during unexpected events. MASIR is based on a decentralized and collaborative multi-agent approach analyzing the profiles of users interested in a given event in order to detect the most prominent ones that have to be tracked in real time. Real time monitoring of these users enables a direct access to valuable fresh information. Our experiments shows that MASIR simplifies the real-time detection and tracking of the most prominent users by exploring both the old and fresh information shared during the event and outperforms the standard centrality measures by using a time-sensitive ranking model.

/pdf/masir-a-multi-agent-system-for-real-time-information-59k2kg3111.pdf

MASIR: A Multi-agent System for Real-Time Information Retrieval from Microblogs During Unexpected Events

In digital libraries, users have diverse ways to perform their research according to their objectives. In order to have access to target documents, users almost make sequences of activities that can be considered as processes. It would be useful to represent the behavior of users in their information seeking processes in order to have a global view on users’ interactions with digital libraries and to effectively answer to their requirements. Discovered processes in digital libraries are almost self-defined that may vary in term of significance, structure and results. It is therefore important to automatically build a model that describe users’ behaviors. In this paper, we tested six state-of-the-art process discovery techniques on two datasets collected from users’ interactions with a cultural heritage digital library. Our preliminary evaluation presents the best performing model that can be used to optimize the exploration of documents in digital libraries.

User’s Behavior in Digital Libraries: Process Mining Exploration

Content shared in microblogs during disasters is expressed in various formats and languages. This diversity makes the information retrieval process more complex and computationally infeasible in real time. To address this, we propose a classification model for the identification of prominent users who are sharing relevant and exclusive information during the disaster. Users who have shared at least one tweet about the disaster are modeled using three kinds of time-sensitive features, including topical, social and geographical features. Then, these users are classified into two classes using a linear Support Vector Machine (SVM) to evaluate them over the extracted features and identify the most prominent ones. The first results using the actual dataset, show that our model has a high accuracy by detecting most of the prominent users. Moreover, we demonstrate that all the proposed features used by our model are indispensable to achieve this high accuracy.

https://hal.archives-ouvertes.fr/hal-01287164/document

Jacques Morcos

Papers

Prominent Users Detection during Specific Events by Learning On- and Off-topic Features of User Activities

MASIR: A Multi-agent System for Real-Time Information Retrieval from Microblogs During Unexpected Events

User’s Behavior in Digital Libraries: Process Mining Exploration

A Classification Model for the Identification of Prominent Microblogs Users during a Disaster