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Proceedings Article

Emoticon Style: Interpreting Differences in Emoticons Across Cultures

TL;DR: This paper investigates the semantic, cultural, and social aspects of emoticon usage on Twitter and shows that emoticons are not limited to conveying a specific emotion or used as jokes, but rather are socio-cultural norms, whose meaning can vary depending on the identity of the speaker.
Abstract: Emoticons are a key aspect of text-based communication, and are the equivalent of nonverbal cues to the medium of online chat, forums, and social media like Twitter. As emoticons become more widespread in computer mediated communication, a vocabulary of different symbols with subtle emotional distinctions emerges especially across different cultures. In this paper, we investigate the semantic, cultural, and social aspects of emoticon usage on Twitter and show that emoticons are not limited to conveying a specific emotion or used as jokes, but rather are socio-cultural norms, whose meaning can vary depending on the identity of the speaker. We also demonstrate how these norms propagate through the Twitter @-reply network. We confirm our results on a large-scale dataset of over one billion Tweets from different time periods and countries.

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Citations
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01 Jan 1906

578 citations

Proceedings ArticleDOI
07 Oct 2013
TL;DR: A new method that combines existing approaches, providing the best coverage results and competitive agreement is developed and a free Web service called iFeel is presented, which provides an open API for accessing and comparing results across different sentiment methods for a given text.
Abstract: Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.

362 citations

Journal ArticleDOI
TL;DR: An integrated view of big data is introduced, the evolution ofbig data over the past 20 years is traced, data analytics essential for processing various structured and unstructured data is discussed, and the application of data analytics using merchant review data is illustrated.

343 citations

Proceedings Article
01 Sep 2013
TL;DR: A novel approach to system combination for the case where available taggers use different tagsets, based on voteconstrained bootstrapping with unlabeled data, reaches 88.7% tagging accuracy, a new high in PTB-compatible tweet part-of-speech tagging.
Abstract: Part-of-speech information is a pre-requisite in many NLP algorithms. However, Twitter text is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style. We present a detailed error analysis of existing taggers, motivating a series of tagger augmentations which are demonstrated to improve performance. We identify and evaluate techniques for improving English part-of-speech tagging performance in this genre. Further, we present a novel approach to system combination for the case where available taggers use different tagsets, based on voteconstrained bootstrapping with unlabeled data. Coupled with assigning prior probabilities to some tokens and handling of unknown words and slang, we reach 88.7% tagging accuracy (90.5% on development data). This is a new high in PTB-compatible tweet part-of-speech tagging, reducing token error by 26.8% and sentence error by 12.2%. The model, training data and tools are made available.

280 citations


Cites background from "Emoticon Style: Interpreting Differ..."

  • ...- - (Park et al., 2013), which was implemented again with high-accuracy regular expressions....

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  • ...Some flexibility is required to capture smiley variations, e.g. - - vs. - - (Park et al., 2013), which was implemented again with high-accuracy regular expressions....

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Journal ArticleDOI
TL;DR: This article proposes a participatory cultural mapping approach based on collective behavior in LBSNs, and shows that the approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.
Abstract: Culture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usually rely on large-scale survey data with respect to human beliefs, such as moral values. However, such a data collection method not only incurs a significant cost of both human resources and time, but also fails to capture human behavior, which massively reflects cultural information. In addition, it is practically difficult to collect large-scale human behavior data. Fortunately, with the recent boom in Location-Based Social Networks (LBSNs), a considerable number of users report their activities in LBSNs in a participatory manner, which provides us with an unprecedented opportunity to study large-scale user behavioral data. In this article, we propose a participatory cultural mapping approach based on collective behavior in LBSNs. First, we collect the participatory sensed user behavioral data from LBSNs. Second, since only local users are eligible for cultural mapping, we propose a progressive “home” location identification method to filter out ineligible users. Third, by extracting three key cultural features from daily activity, mobility, and linguistic perspectives, respectively, we propose a cultural clustering method to discover cultural clusters. Finally, we visualize the cultural clusters on the world map. Based on a real-world LBSN dataset, we experimentally validate our approach by conducting both qualitative and quantitative analysis on the generated cultural maps. The results show that our approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.

243 citations


Cites background from "Emoticon Style: Interpreting Differ..."

  • ...[Park et al. 2013] investigated the cultural differences on the usage of facial expressions for emotion in Twitter....

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References
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Journal ArticleDOI
TL;DR: The Linguistic Inquiry and Word Count (LIWC) system as discussed by the authors is a text analysis system that counts words in psychologically meaningful categories to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles and individual differences.
Abstract: We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences.

4,356 citations


"Emoticon Style: Interpreting Differ..." refers methods in this paper

  • ...Inferring Affect From Tweets In order to quantitatively measure what kinds of affect are associated with a given emoticon, we used LIWC (Linguistic Inquiry and Word Count) (Tausczik and Pennebaker 2010), which is a text analysis program that counts words in various psychological categories....

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Proceedings Article
16 May 2010
TL;DR: An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
Abstract: Directed links in social media could represent anything from intimate friendships to common interests, or even a passion for breaking news or celebrity gossip. Such directed links determine the flow of information and hence indicate a user's influence on others — a concept that is crucial in sociology and viral marketing. In this paper, using a large amount of data collected from Twitter, we present an in-depth comparison of three measures of influence: indegree, retweets, and mentions. Based on these measures, we investigate the dynamics of user influence across topics and time. We make several interesting observations. First, popular users who have high indegree are not necessarily influential in terms of spawning retweets or mentions. Second, most influential users can hold significant influence over a variety of topics. Third, influence is not gained spontaneously or accidentally, but through concerted effort such as limiting tweets to a single topic. We believe that these findings provide new insights for viral marketing and suggest that topological measures such as indegree alone reveals very little about the influence of a user.

3,041 citations


"Emoticon Style: Interpreting Differ..." refers methods in this paper

  • ...Methodology Twitter Data We use a corpus of the Twitter data in (Cha et al. 2010) from 2006 to 2009, which contains information about 54 million users and all of their public posts....

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Journal ArticleDOI
TL;DR: In this article, the emergence and stability of behavioral norms in the context of a game played by people of limited rationality is analyzed with a computer simulation based upon the evolutionary principle that strategies shown to be relatively effective will be used more in the future than less effective strategies.
Abstract: Iorms provide a powerful mechanism for regulating conflict in groups, even when there are more than two people and no central authority. This paper investigates the emergence and stability of behavioral norms in the context of a game played by people of limited rationality. The dynamics of this new norms game are analyzed with a computer simulation based upon the evolutionary principle that strategies shown to be relatively effective will be used more in the future than less effective strategies. The results show the conditions under which norms can evolve and prove stable. One interesting possibility is the employment of metanorms, the willingness to punish someone who did not enforce a norm. Many historical examples of domestic and international norms are used to illustrate the wide variety of mechanisms that can support norms, including metanorms, dominance, internalization, deterrence, social proof, membership in groups, law, and reputation.

1,761 citations


"Emoticon Style: Interpreting Differ..." refers background in this paper

  • ...Foundational studies of influence (Axelrod 1986) and homophily (Lazarsfeld and R.K.Merton 1954) analyze these mechanisms on their own, but more recently researchers have begun to look at how influence and homophily interact (Axelrod 1997), especially in the context of social media to lead to…...

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  • ...Foundational studies of influence (Axelrod 1986) and homophily (Lazarsfeld and R....

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Journal ArticleDOI
TL;DR: In this paper, an agent-based adaptive model is proposed to reveal the effects of a mechanism of convergent social influence, where actors are placed at fixed sites and the basic premise is that the more similar an actor is to a neighbor, the more likely that that actor will adopt one of the neighbor's traits.
Abstract: Despite tendencies toward convergence, differences between individuals and groups continue to exist in beliefs, attitudes, and behavior. An agent-based adaptive model reveals the effects of a mechanism of convergent social influence. The actors are placed at fixed sites. The basic premise is that the more similar an actor is to a neighbor, the more likely that that actor will adopt one of the neighbor's traits. Unlike previous models of social influence or cultural change that treat features one at a time, the proposed model takes into account the interaction between different features. The model illustrates how local convergence can generate global polarization. Simulations show that the number of stable homogeneous regions decreases with the number of features, increases with the number of alternative traits per feature, decreases with the range of interaction, and (most surprisingly) decreases when the geographic territory grows beyond a certain size. MAINTENANCE OF DIFFERENCES If people tend to become more alike in their beliefs, attitudes, and behavior when they interact, why do not all such differences eventually disappear? Social scientists have proposed many mechanisms to answer this question. The purpose of this article is to explore one more mechanism. The mechanism proposed here deals with how people do indeed become more similar as they interact, but also provides an explanation of why the tendency to converge stops before it reaches completion. It therefore provides a new type of explanation of why we do not all become alike. Because the proposed mechanism can exist alongside other mechanisms, it can be regarded as complementary with older explanations rather than necessarily competing with them. Unfortunately, no good term describes the range of things about which people can influence each other. Although beliefs, attitudes, and behavior cover a wide range indeed, there are still more things over which interpersonal influence extends, such as language, art, technical standards, and social norms. The most generic term for the

1,754 citations


"Emoticon Style: Interpreting Differ..." refers background in this paper

  • ...Merton 1954) analyze these mechanisms on their own, but more recently researchers have begun to look at how influence and homophily interact (Axelrod 1997), especially in the context of social media to lead to complex effects on the behavior and link patterns of individuals....

    [...]

  • ...…(Lazarsfeld and R.K.Merton 1954) analyze these mechanisms on their own, but more recently researchers have begun to look at how influence and homophily interact (Axelrod 1997), especially in the context of social media to lead to complex effects on the behavior and link patterns of individuals....

    [...]