Tapping into sociological lexicons for sentiment polarity classification

Sentiment Analysis, or the extraction of emotional content from text, has been a prominent research topic for a decade. Numerous annotated lexicons have been created for identification and classification of emotions (or affect) in text. This extraction of emotional content from text makes possible emotion-aware Information Retrieval, which is especially important with the growing popularity of user-generated content like blogs, tweets, and wikis. This paper introduces a new source of high quality manual annotations that can be used for sentiment extraction. A subfield of sociology symbolic interactionism, more precisely Affect Control Theory (ACT), measures the emotional meanings we associate with various concepts. Research in this field produces multi-dimensional manual annotations of words much like those used in Sentiment Analysis. We compare these annotations with SentiWordNet and WordNet-Affect, lexicons produced for Sentiment Analysis, in the task of text polarity classification and show that classifier trained on the ACT lexicon outperforms the other two

Fetishizing Food in Digital Age: #foodporn Around the World

International AAAI Conference on Web and Social Media (ICWSM), 2016International AAAI Conference on Web and Social Media (ICWSM), 2016What food is so good as to be considered pornographic? Worldwide, the popular #foodporn hashtag has been used to share appetizing pictures of peoples' favorite culinary experiences. But social scientists ask whether #foodporn promotes an unhealthy relationship with food, as pornography would contribute to an unrealistic view of sexuality. In this study, we examine nearly 10 million Instagram posts by 1.7 million users worldwide. An overwhelming (and uniform across the nations) obsession with chocolate and cake shows the domination of sugary dessert over local cuisines. Yet, we find encouraging traits in the association of emotion and health-related topics with #foodporn, suggesting food can serve as motivation for a healthy lifestyle. Social approval also favors the healthy posts, with users posting with healthy hashtags having an average of 1,000 more followers than those with unhealthy ones. Finally, we perform a demographic analysis which shows nation-wide trends of behavior, such as a strong relationship (r=0.51) between the GDP per capita and the attention to healthiness of their favorite food. Our results expose a new facet of food "pornography", revealing potential avenues for utilizing this precarious notion for promoting healthy lifestyles

#FoodPorn: Obesity Patterns in Culinary Interactions

We present a large-scale analysis of Instagram pictures taken at 164,753 restaurants by millions of users. Motivated by the obesity epidemic in the United States, our aim is three-fold: (i) to assess the relationship between fast food and chain restaurants and obesity, (ii) to better understand people's thoughts on and perceptions of their daily dining experiences, and (iii) to reveal the nature of social reinforcement and approval in the context of dietary health on social media. When we correlate the prominence of fast food restaurants in US counties with obesity, we find the Foursquare data to show a greater correlation at 0.424 than official survey data from the County Health Rankings would show. Our analysis further reveals a relationship between small businesses and local foods with better dietary health, with such restaurants getting more attention in areas of lower obesity. However, even in such areas, social approval favors the unhealthy foods high in sugar, with donut shops producing the most liked photos. Thus, the dietary landscape our study reveals is a complex ecosystem, with fast food playing a role alongside social interactions and personal perceptions, which often may be at odds

Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data

Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress toward such an ambitious target, reliable, up-to-date and fine-grained measurements of socioeconomic indicators are necessary. When it comes to socioeconomic development, novel digital traces can provide a complementary data source to overcome the limits of traditional data collection methods, which are often not regularly updated and lack adequate spatial resolution. In this study, we collect publicly available and anonymous advertising audience estimates from Facebook to predict socioeconomic conditions of urban residents, at a fine spatial granularity, in four large urban areas: Atlanta (USA), Bogotá (Colombia), Santiago (Chile), and Casablanca (Morocco). We find that behavioral attributes inferred from the Facebook marketing platform can accurately map the socioeconomic status of residential areas within cities, and that predictive performance is comparable in both high and low-resource settings. Our work provides additional evidence of the value of social advertising media data to measure human development and it also shows the limitations in generalizing the use of these data to make predictions across countries

Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web

In the Web Science Track of 26th International World Wide Web Conference (WWW 2017)In the Web Science Track of 26th International World Wide Web Conference (WWW 2017)In the Web Science Track of 26th International World Wide Web Conference (WWW 2017)Food and nutrition occupy an increasingly prevalent space on the web, and dishes and recipes shared online provide an invaluable mirror into culinary cultures and attitudes around the world. More specifically, ingredients, flavors, and nutrition information become strong signals of the taste preferences of individuals and civilizations. However, there is little understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences across the globe

Reducing Controversy by Connecting Opposing Views

Peer reviewe

A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table

Twitter ain't without frontiers: economic, social, and cultural boundaries in international communication

With the advent of Twitter and other lightweight social-networking services, one might think that it is easier than ever to maintain geographically dispersed, weaker social ties. By contrast, in this study we show that the international Twitter communication landscape is not only still largely predetermined by physical distance, but that it also depends on countries' social, economic, and cultural attributes. We describe a study of an international Twitter mention network of 13 million users across over 100 countries. We show that the Gravity Model, which hypothesizes that the flow between two areas is proportional to their masses (which we approximate using internet penetration) and inversely proportional to the distance between them, is correlated (r=0.68) with the international communication flow. Using this model, along with other social, economic, and cultural variables, we predict the communication volume at Adjusted R2 of $0.80$, with trade, language and racial intolerance especially impacting communication. We discuss the implications of these barriers to communication in the contexts of collaborative work, software design, and recommendation systems

Twitter ain't without frontiers: economic, social, and cultural boundaries in international communication

With the advent of Twitter and other lightweight social-networking services, one might think that it is easier than ever to maintain geographically dispersed, weaker social ties. By contrast, in this study we show that the international Twitter communication landscape is not only still largely predetermined by physical distance, but that it also depends on countries' social, economic, and cultural attributes. We describe a study of an international Twitter mention network of 13 million users across over 100 countries. We show that the Gravity Model, which hypothesizes that the flow between two areas is proportional to their masses (which we approximate using internet penetration) and inversely proportional to the distance between them, is correlated (r=0.68) with the international communication flow. Using this model, along with other social, economic, and cultural variables, we predict the communication volume at Adjusted R2 of $0.80$, with trade, language and racial intolerance especially impacting communication. We discuss the implications of these barriers to communication in the contexts of collaborative work, software design, and recommendation systems