Jahna Otterbacher

Evolutionary timeline summarization: a balanced optimization framework via iterative substitution

Classic news summarization plays an important role with the exponential document growth on the Web. Many approaches are proposed to generate summaries but seldom simultaneously consider evolutionary characteristics of news plus to traditional summary elements. Therefore, we present a novel framework for the web mining problem named Evolutionary Timeline Summarization (ETS). Given the massive collection of time-stamped web documents related to a general news query, ETS aims to return the evolution trajectory along the timeline, consisting of individual but correlated summaries of each date, emphasizing relevance, coverage, coherence and cross-date diversity. ETS greatly facilitates fast news browsing and knowledge comprehension and hence is a necessity. We formally formulate the task as an optimization problem via iterative substitution from a set of sentences to a subset of sentences that satisfies the above requirements, balancing coherence/diversity measurement and local/global summary quality. The optimized substitution is iteratively conducted by incorporating several constraints until convergence. We develop experimental systems to evaluate on 6 instinctively different datasets which amount to 10251 documents. Performance comparisons between different system-generated timelines and manually created ones by human editors demonstrate the effectiveness of our proposed framework in terms of ROUGE metrics.

Inferring gender of movie reviewers: exploiting writing style, content and metadata

Despite differences in the way that men and women experience goods and communicate their perspectives, online review communities typically do not provide participants gender. We propose to infer author gender, given a set of reviews of a particular item, and experiment on reviews posted at the Internet Movie Database (IMDb). Using logistic regression, we explore the contribution of three types of information: 1) style, 2) content, and 3) metadata (e.g. review age, social feedback). Our results concur with previous research, in that there are salient differences in writing style and content between reviews authored by men versus women. However, in comparison to literary or scientific texts, to which classification tasks are often applied, reviews are brief and occur within the context of an ongoing discourse. Therefore, to compensative for the brevity of reviews, content and stylistic features can be augmented with metadata. We find in particular that the perceived utility of a review is an important correlate of gender. The model incorporating all features has a classification accuracy of 73.7% and is not as sensitive to review length as are those based only on stylistic or content features.

What's congress doing on twitter?

As Twitter becomes a more common means for officials to communicate with their constituents, it becomes more important that we understand how officials use these communication tools. Using data from 380 members of Congress Twitter activity during the winter of 2012, we find that officials frequently use Twitter to advertise their political positions and to provide information but rarely to request political action from their constituents or to recognize the good work of others. We highlight a number of differences in communication frequency between men and women, Senators and Representatives, Republicans and Democrats. We provide groundwork for future research examining the behavior of public officials online and testing the predictive power of officials social media behavior.

Being Heard in Review Communities: Communication Tactics and Review Prominence

Review communities typically display contributions in list format, using participant feedback in determining presentation order. Given the volume of contributions, which are likely to be seen? While previous work has focused on content, we examine the relationship between communication tactics and prominence. We study three communities, comparing front-page reviews versus those on latter pages. We consider 3 types of devices: structural features, textual features, and persuasive writing. Structural features, such as profiles, convey information about authors. Textual properties, such as punctuation use, can make an impression on others. Rhetorical writing strategies are used by reviewers to convince readers of their opinions. When controlling for content, the most salient tactics distinguishing prominent reviews are textual properties and persuasive language.

Learning the lingo?: gender, prestige and linguistic adaptation in review communities

Women and men communicate differently in both face-to-face and computer-mediated environments. We study linguistic patterns considered gendered in reviews contributed to the Internet Movie Database. IMDb has been described as a male-majority community, in which females contribute fewer reviews and enjoy less prestige than males. Analyzing reviews posted by prolific males and females, we hypothesize that females adjust their communication styles to be in sync with their male counterparts. We find evidence that while certain characteristics of female language persevere over time (e.g., frequent use of pronouns) others (e.g., hedging) decrease with time. Surprisingly, we also find that males often increase their use of female features. Our results indicate, that even when they resemble mens reviews linguistically, womens reviews still enjoy less prestige and smaller audiences.

Gender, writing and ranking in review forums: a case study of the IMDb

Online review forums provide consumers with essential information about goods and services by facilitating word-of-mouth communication. Despite that preferences are correlated to demographic characteristics, reviewer gender is not often provided on user profiles. We consider the case of the internet movie database (IMDb), where users exchange views on movies. Like many forums, IMDb employs collaborative filtering such that by default, reviews are ranked by perceived utility. IMDb also provides a unique gender filter that displays an equal number of reviews authored by men and women. Using logistic classification, we compare reviews with respect to writing style, content and metadata features. We find salient differences in stylistic features and content between reviews written by men and women, as predicted by sociolinguistic theory. However, utility is the best predictor of gender, with women’s reviews perceived as being much less useful than those written by men. While we cannot observe who votes at IMDb, we do find that highly rated female-authored reviews exhibit “male” characteristics. Our results have implications for which contributions are likely to be seen, and to what extent participants get a balanced view as to “what others think” about an item.

Helpful to you is useful to me: The use and interpretation of social voting

Social voting plays a key role in the organization of user-contributed content; readers are asked to indicate what they “like” or find “helpful,” with collected votes then used to prioritize valued content. Despite the popularity of these mechanisms, little is known as to how users employ and interpret this feedback. We conducted a study in which participants researched items at two review communities, keeping lists of reviews they found helpful and not. We observed their behaviors and asked them to rate reviews on several dimensions. We found consensus that helpful contributions are clearly written, relevant to users needs, and express an appropriate amount of information. We also observed that users relied on others judgments, attending to the most helpful content. We discuss implications, when users behave as though what is helpful to others is helpful to them.