Social Bots: Human-Like by Means of Human Control?

Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term `Social Bot' is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to go in order to realize effective interactions with other humans.Comment: 36 pages, 13 figure

Predictive Analysis on Twitter: Techniques and Applications

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

Mining Behavior of Citizen Sensor Communities to Improve Cooperation with Organizational Actors

Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including \u27articulation\u27 and \u27awareness\u27, but Web 2.0 supported CSC offers new challenges as well as opportunities. A CSC presents information overload for the organizational actors, especially in finding reliable information providers (for awareness), and finding actionable information from the data generated by citizens (for articulation). Also, we note three data level challenges: ambiguity in interpreting unconstrained natural language text, sparsity of user behaviors, and diversity of user demographics. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues. I present a novel web information-processing framework, called the Identify-Match- Engage (IME) framework. IME allows operationalizing computation in design problems of awareness and articulation of the cooperative system between citizens and organizations, by addressing data problems of group engagement modeling and intent mining. The IME framework includes: a.) Identification of cooperation-assistive intent (seeking-offering) from short, unstructured messages using a classification model with declarative, social and contrast pattern knowledge, b.) Facilitation of coordination modeling using bipartite matching of complementary intent (seeking-offering), and c.) Identification of user groups to prioritize for engagement by defining a content-driven measure of \u27group discussion divergence\u27. The use of prior knowledge and interplay of features of users, content, and network structures efficiently captures context for computing cooperation-assistive behavior (intent and engagement) from unstructured social data in the online socio-technical systems. Our evaluation of a use-case of the crisis response domain shows improvement in performance for both intent classification and group engagement prioritization. Real world applications of this work include use of the engagement interface tool during various recent crises including the 2014 Jammu and Kashmir floods, and intent classification as a service integrated by the crisis mapping pioneer Ushahidi\u27s CrisisNET project for broader impact

Detecting Abusive Language on Online Platforms: A Critical Analysis

Abusive language on online platforms is a major societal problem, often leading to important societal problems such as the marginalisation of underrepresented minorities. There are many different forms of abusive language such as hate speech, profanity, and cyber-bullying, and online platforms seek to moderate it in order to limit societal harm, to comply with legislation, and to create a more inclusive environment for their users. Within the field of Natural Language Processing, researchers have developed different methods for automatically detecting abusive language, often focusing on specific subproblems or on narrow communities, as what is considered abusive language very much differs by context. We argue that there is currently a dichotomy between what types of abusive language online platforms seek to curb, and what research efforts there are to automatically detect abusive language. We thus survey existing methods as well as content moderation policies by online platforms in this light, and we suggest directions for future work

Response Retrieval in Information-seeking Conversations

The increasing popularity of mobile Internet has led to several crucial changes in the way that people use search engines compared with traditional Web search on desktops. On one hand, there is limited output bandwidth with the small screen sizes of most mobile devices. Mobile Internet users prefer direct answers on the search engine result page (SERP). On the other hand, voice-based / text-based conversational interfaces are becoming increasing popular as shown in the wide adoption of intelligent assistant services and devices such as Amazon Echo, Microsoft Cortana and Google Assistant around the world. These important changes have triggered several new challenges that search engines have had to adapt to in order to better satisfy the information needs of mobile Internet users. In this dissertation, we investigate several aspects of single-turn answer retrieval and multi-turn information-seeking conversations to handle the new challenges of search on the mobile Internet. We start from the research on single-turn answer retrieval and analyze the weaknesses of existing deep learning architectures for answer ranking. Then we propose an attention based neural matching model with a value-shared weighting scheme and attention mechanism to improve existing deep neural answer ranking models. Our proposed model achieves state-of-the-art performance for answer sentence retrieval compared with both feature engineering based methods and other neural models. Then we move on to study response retrieval in multi-turn information-seeking conversations beyond single-turn interactions. Much research on response selection in conversation systems is modeling the matching patterns between user input message (either with context or not) and response candidates, which ignores external knowledge beyond the dialog utterances. We propose a learning framework on top of deep neural matching networks that leverages external knowledge with pseudo-relevance feedback and QA correspondence knowledge distillation for response retrieval. We also study how to integrate user intent modeling into neural ranking models to improve response retrieval performance. Finally, hybrid models of response retrieval and generation are investigated in order to combine the merits of these two different paradigms of conversation models. Our goal is to develop effective learning models for answer retrieval and information-seeking conversations, in order to improve the effectiveness and user experience when accessing information with a touch screen interface or a conversational interface, as commonly adopted by millions of mobile Internet devices

Multimodal Sentiment Analysis Based on Deep Learning: Recent Progress

Multimodal sentiment analysis is an important research topic in the field of NLP, aiming to analyze speakers\u27 sentiment tendencies through features extracted from textual, visual, and acoustic modalities. Its main methods are based on machine learning and deep learning. Machine learning-based methods rely heavily on labeled data. But deep learning-based methods can overcome this shortcoming and capture the in-depth semantic information and modal characteristics of the data, as well as the interactive information between multimodal data. In this paper, we survey the deep learning-based methods, including fusion of text and image and fusion of text, image, audio, and video. Specifically, we discuss the main problems of these methods and the future directions. Finally, we review the work of multimodal sentiment analysis in conversation