Social network sites SNSs are increasingly attracting the attention of academic and industry researchers intrigued by their affordances and reach This special theme section of the Journal of Computer-Mediated Communication brings together scholarship on these emergent phenomena In this introductory article, we describe features of SNSs and propose a comprehensive definition We then present one perspective on the history of such sites, discussing key changes and developments After briefly summarizing existing scholarship concerning SNSs, we discuss the articles in this special section and conclude with considerations for future research

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1083-6101.2007.00393.x

Social Network Sites: Definition, History, and Scholarship

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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Opinion Mining and Sentiment Analysis

We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines for VQA are provided and compared with human performance.

/pdf/vqa-visual-question-answering-3vu8bkyw7k.pdf

VQA: Visual Question Answering

Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.

/pdf/a-sentimental-education-sentiment-analysis-using-3khq10wbkz.pdf

A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

Social media technologies collapse multiple audiences into single contexts, making it difficult for people to use the same techniques online that they do to handle multiplicity in face-to-face conversation. This article investigates how content producers navigate ‘imagined audiences’ on Twitter. We talked with participants who have different types of followings to understand their techniques, including targeting different audiences, concealing subjects, and maintaining authenticity. Some techniques of audience management resemble the practices of ‘micro-celebrity’ and personal branding, both strategic self-commodification. Our model of the networked audience assumes a many-to-many communication through which individuals conceptualize an imagined audience evoked through their tweets.

/pdf/i-tweet-honestly-i-tweet-passionately-twitter-users-context-g4xm6zfq7s.pdf

I Tweet Honestly, I Tweet Passionately: Twitter Users, Context Collapse, and the Imagined Audience

ConceptNet is a freely available commonsense knowledge base and natural-language-processing tool-kit which supports many practical textual-reasoning tasks over real-world documents including topic-gisting, analogy-making, and other context oriented inferences. The knowledge base is a semantic network presently consisting of over 1.6 million assertions of commonsense knowledge encompassing the spatial, physical, social, temporal, and psychological aspects of everyday life. ConceptNet is generated automatically from the 700 000 sentences of the Open Mind Common Sense Project — a World Wide Web based collaboration with over 14 000 authors.

/pdf/conceptnet-a-practical-commonsense-reasoning-tool-kit-3723gsymn1.pdf

ConceptNet — A Practical Commonsense Reasoning Tool-Kit

This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and hand-crafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories. This commonsense approach has new robustness implications.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text. Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.

/pdf/a-model-of-textual-affect-sensing-using-real-world-knowledge-3onvgfnin9.pdf

A model of textual affect sensing using real-world knowledge

This study examines how a social network profile's lists of interests-music, books, movies, television shows, etc.-can function as an expressive arena for taste performance. By composing interest tokens around a theme, profile users craft their "taste statements." First, socioeconomic and aesthetic influences on taste are considered, and the expressivity of interest tokens is analyzed using a semiotic framework. Then, a grounded theory approach is taken to identify four types of taste statements-those that convey prestige, differentiation, authenticity, and theatrical persona. The semantics of taste and taste statements are further investigated through a statistical analysis of 127,477 profiles collected from the MySpace social network site between November 2006 and January 2007. The major findings of the analysis include statistical evidence for prestige and differentiation taste statements and an interpretation of the taste semantics underlying the MySpace community-its motifs, paradigms, and demographic structures.

https://www.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1083-6101.2007.00395.x

Social Network Profiles as Taste Performances

What are the sources of happiness and sadness in everyday life? In this paper, we employ ‘linguistic ethnography’ to seek out where happiness lies in our everyday lives by considering a corpus of blogposts from the LiveJournal community annotated with happy and sad moods. By analyzing this corpus, we derive lists of happy and sad words and phrases annotated by their ‘happiness factor.’ Various semantic analyses performed with this wordlist reveal the happiness trajectory of a 24-day (3am and 9-10p are most happy), and a 7-day week (Wednesdays are saddest), and compare the socialness and humancenteredness of happy descriptions versus sad descriptions. We evaluate our corpus-based approach in a classification task and contrast our wordlist with emotionally-annotated wordlists produced by experimental focus groups. Having located happiness temporally and semantically within this corpus of everyday life, the paper concludes by offering a corpus-inspired livable recipe for happiness 1 .

/pdf/a-corpus-based-approach-to-finding-happiness-4ymakp353h.pdf

A Corpus-based Approach to Finding Happiness

ConceptNet is a very large semantic network of commonsense knowledge suitable for making various kinds of practical inferences over text. ConceptNet captures a wide range of commonsense concepts and relations like those in Cyc, while its simple semantic network structure lends it an ease-of-use comparable to WordNet. To meet the dual challenge of having to encode complex higher-order concepts, and maintaining ease-of-use, we introduce a novel use of semi-structured natural language fragments as the knowledge representation of commonsense concepts. In this paper, we present a methodology for reasoning flexibly about these semi-structured natural language fragments. We also examine the tradeoffs associated with representing commonsense knowledge in formal logic versus in natural language. We conclude that the flexibility of natural language makes it a highly suitable representation for achieving practical inferences over text, such as context finding, inference chaining, and conceptual analogy.

/pdf/commonsense-reasoning-in-and-over-natural-language-22fi2jbf56.pdf

Hugo Liu

Papers

ConceptNet — A Practical Commonsense Reasoning Tool-Kit

A model of textual affect sensing using real-world knowledge

Social Network Profiles as Taste Performances

A Corpus-based Approach to Finding Happiness

Commonsense Reasoning in and Over Natural Language